Ask A Manager 2021

# load "Ask a A Manager 2021 Survey" googlesheet
# https://www.askamanager.org/
ask_a_manager_2021 <- read_csv(here::here("data","ask_a_manager.csv"))

skimr::skim(ask_a_manager_2021)

Data summary

Name	ask_a_manager_2021
Number of rows	26765
Number of columns	18
_______________________
Column type frequency:
character	14
logical	2
numeric	1
POSIXct	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
how_old_are_you	0	1.00	5	10	0	7	0
industry	62	1.00	2	171	0	1084	0
job_title	0	1.00	1	126	0	12838	0
additional_context_on_job_title	19868	0.26	1	781	0	6612	0
currency	0	1.00	3	7	0	11	0
additional_context_on_income	23851	0.11	1	1143	0	2839	0
country	0	1.00	1	209	0	297	0
state	4761	0.82	4	114	0	125	0
city	23	1.00	1	171	0	4070	0
overall_years_of_professional_experience	0	1.00	9	16	0	8	0
years_of_experience_in_field	0	1.00	9	16	0	8	0
highest_level_of_education_completed	202	0.99	3	34	0	6	0
gender	155	0.99	3	29	0	5	0
race	151	0.99	5	125	0	47	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
other_monetary_comp	26765	0	NaN	:
currency_other	26765	0	NaN	:

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
annual_salary	0	1	144988	5488158	0	54000	75712	110000	8.7e+08	▇▁▁▁▁

Variable type: POSIXct

skim_variable	n_missing	complete_rate	min	max	median	n_unique
timestamp	0	1	2021-04-27 11:02:09	2021-09-14 21:55:44	2021-04-28 12:35:21	23989

Data cleaning

After skimming the dataset, we took the below steps to clean the raw data :

• Removing NA values
• Removing outliers (out of \([25\%-1.5\times IQR,75\%+1.5\times IQR]\))
• Transforming certain datatype into factor
• Removing problematic entries (some people under 18 years old have more than 18 years experience)
• Selecting key industries from 1000+ industries reported
• Unifying currencies

Removing NA values

# remove the NA values and 0 salaries
ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  filter(!gender %in% c("NA","Other or prefer not to answer","Prefer not to answer", "Non-binary", NA) &
           race!="NA" &
           highest_level_of_education_completed!="NA"&
           annual_salary>0)

Removing outliers

# using z-score. remove data out of 3 sigma
# ask_a_manager_2021 <- ask_a_manager_2021[-which(abs(scale(ask_a_manager_2021$annual_salary))>3),]

# using quantile. remove data larger than 75%+1.5*IQR or smaller than 25%-1.5*IQR
# we choose this method recommended by the stats community
Q <- quantile(ask_a_manager_2021$annual_salary, probs=c(.25, .75))
iqr <- IQR(ask_a_manager_2021$annual_salary)
up <-  Q[2]+1.5*iqr # Upper Range
low <-  Q[1]-1.5*iqr # Upper Range
ask_a_manager_2021 <- ask_a_manager_2021 %>%
  filter(annual_salary>=low & annual_salary<=up)

Removing very small age group

We observe there are only 2 samples under 18 years old correctly entered their data, so we remove them to make analysis between age groups possible.

ask_a_manager_2021 %>% 
  group_by(how_old_are_you, gender) %>% 
  summarise(count=n()) %>% 
  ggplot(aes(x=fct_relevel(how_old_are_you,levels=c("under 18","18-24","25-34","35-44","45-54","55-64","65 or over")),y=count))+
  geom_col()+
  labs(title="Sample size in age groups",
       x="Age Group",
       y="number of samples")+
  NULL

ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  filter(how_old_are_you!="under 18")

Transforming into ordered factor datatype

Namely, we want to make age, years of working experience, education into factor, and take the log of salary for the next step of EDA.

ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  mutate(
    country = countrycode::countryname(country),
    age_group = factor(how_old_are_you,levels=c("18-24","25-34","35-44","45-54","55-64","65 or over")),
    field_exp = factor(years_of_experience_in_field,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")),
    pro_exp = factor(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")),
    education = factor(highest_level_of_education_completed, levels=c("High School","Some college","College degree","Master's degree","Professional degree (MD, JD, etc.)", "PhD")),
     higher_edu = ifelse(education %in% c("College degree","Master's degree", "PhD", "Professional degree (MD, JD, etc.)"),1,0),
    salary = annual_salary,
    # take ln() to annual salary
    log_salary = log(annual_salary)
  ) %>% 
  janitor::clean_names() # clean columns names

Remove problematic entries

# there are people under 18 having more than 18 years of experience
ask_a_manager_2021 %>% 
  filter(age_group=="under 18") %>% 
  select(c("age_group","overall_years_of_professional_experience"))

age_group	overall_years_of_professional_experience

ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  mutate(max_age = case_when(how_old_are_you=="under 18"~18,
                             how_old_are_you=="65 or over"~999,
                             T~as.numeric(substr(how_old_are_you,4,5))),
         # parse the first number in a character
         min_exp = if_else(parse_number(overall_years_of_professional_experience)>
                       parse_number(years_of_experience_in_field),
                       parse_number(overall_years_of_professional_experience),
                       parse_number(years_of_experience_in_field))) %>% 
  # delete the problematic entries and the tow columns created here.
  filter(max_age>=min_exp) %>% 
  select(-c("max_age","min_exp"))

Selecting industries

Since there are over 1000 reported industries in the dataset, we will only keep the top 25 industries which account for 92% of total entries

# Industry is messy... it has > 1000 different industries.
# we only keep the names of top 25 industries. (24597(92%) out of 26765 entries)
industry_list <- ask_a_manager_2021 %>% 
  count(industry, sort=TRUE) %>% 
  mutate(percent = 100* n/sum(n)) %>% 
  slice_max(order_by = n, n = 25) %>% 
  select(industry)

ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  mutate(industry = ifelse(industry %in% industry_list$industry,
                           industry,
                           "Others"))

Unifying currencies

Since there are 11 different currencies reported in the dataset, we want to unify the most common currencies into USD for the convenience of comparison analysis afterwards. We decided to convert all currencies with occurences greater than 100 into USD. This included CAD,GBP,EUR,AUD/NZD. Other was not converted as there wasn’t specific information on the currency.

ask_a_manager_2021 %>%
  count(currency, sort=TRUE) %>%
  mutate(percent = 100* n/sum(n))

currency	n	percent
USD	20268	83.6
CAD	1474	6.08
GBP	1434	5.91
EUR	551	2.27
AUD/NZD	432	1.78
Other	57	0.235
CHF	29	0.12
SEK	1	0.00412

From the above table, We can see that USD, CAD, GBP, EUR, (AUD/NZD) are the most common currencies. We will convert the currencies all to USD using exchange rate data from library quantmod, and the exchange rate will be taken from the last date of the survey “2021-09-14”

#gets the currency rate for the currency pairs. This uses the Oanda which keeps currency data for the last 180 days.
#In 180 days from "2021-09-14" the code will no longer return the currency rates.
currencies <-getSymbols(c("EUR/USD","CAD/USD","GBP/USD","AUD/USD" , "NZD/USD"),
                        from="2021-09-14", to="2021-09-14", src="oanda")

#In order to preserve the analysis for after 180 days we can hard code the currency rates into a tibble
currencies_preserved <- tibble(
  name = c("EUR/USD","CAD/USD","GBP/USD","AUD/USD" , "NZD/USD"),
  value = c(1.18,0.79,1.38,0.734,0.711)
)

Here we create a new column called converted_salary which converts the 5 most common currencies in USD. These are then rounded to the nearest whole number.

#the values are taken from the currencies_preserved$value as to preserve the analysis when 
# getSymbols will no longer work. The index matches the currency pair.
ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  mutate(converted_salary = case_when(
    currency == "GBP" ~ round(annual_salary*currencies_preserved$value[3]),
    currency == "AUD/NZD" & country == "Australia" ~ round(annual_salary*currencies_preserved$value[4]),
    currency == "AUD/NZD" & country == "New Zealand" ~ round(annual_salary*currencies_preserved$value[5]),
    currency == "CAD" ~ round(annual_salary*currencies_preserved$value[2]),
    currency == "EUR" ~ round(annual_salary*currencies_preserved$value[1]),
    TRUE ~ round(annual_salary)
  ))

#Here we filter the dataset to only include the salaries that were converted into USD terms. The other currencies also included lots of outliers which we also get rid of here.

ask_a_manager_2021 <- ask_a_manager_2021 %>% 
  filter(currency %in% c("USD","GBP","AUD/NZD","EUR","CAD") &
           !is.na(country))

ask_a_manager_2021 %>%
  count(currency, sort=TRUE) %>%
  mutate(percent = 100* n/sum(n))

currency	n	percent
USD	20128	84.1
CAD	1464	6.12
GBP	1362	5.69
EUR	547	2.29
AUD/NZD	428	1.79

Exploratory Data Analysis

Counts

# Some quick counts, groups, etc
ask_a_manager_2021 %>% 
  count(age_group, sort=TRUE) %>% 
  mutate(percent = 100* n/sum(n)) %>% 
  ggplot(aes(x=fct_relevel(age_group,levels=c("under 18","18-24","25-34","35-44","45-54","55-64","65 or over")), y=n)) +
<<<<<<< HEAD
  geom_bar(stat="identity") + 
  labs(title = "Age distribution of Ask a Manager survey respondents",
       x="Age group",
       y="Frequency",                                                  
       caption = "Source:Askamanager.com")

# 'country' 
ask_a_manager_2021 %>%
  count(country, sort=TRUE) %>% 
  mutate(percent = 100* n/sum(n))

country	n	percent
US	19988	83.5
Canada	1477	6.17
UK	1368	5.72
Australia	338	1.41
Germany	166	0.694
Ireland	111	0.464
New Zealand	100	0.418
Netherlands	76	0.318
France	56	0.234
Spain	41	0.171
Belgium	32	0.134
Austria	14	0.0585
Finland	14	0.0585
Italy	13	0.0543
Denmark	7	0.0293
Israel	6	0.0251
Greece	5	0.0209
Japan	5	0.0209
Portugal	5	0.0209
Sweden	5	0.0209
Brazil	4	0.0167
India	4	0.0167
Mexico	4	0.0167
Puerto Rico	4	0.0167
Romania	4	0.0167
Singapore	4	0.0167
Latvia	3	0.0125
Luxembourg	3	0.0125
Pakistan	3	0.0125
Philippines	3	0.0125
South Africa	3	0.0125
Switzerland	3	0.0125
Bermuda	2	0.00836
Bulgaria	2	0.00836
China	2	0.00836
Estonia	2	0.00836
Ghana	2	0.00836
Kenya	2	0.00836
Lithuania	2	0.00836
Malaysia	2	0.00836
Norway	2	0.00836
Poland	2	0.00836
Thailand	2	0.00836
Afghanistan	1	0.00418
Argentina	1	0.00418
Bahamas	1	0.00418
Bangladesh	1	0.00418
Cambodia	1	0.00418
Cayman Islands	1	0.00418
Chile	1	0.00418
Costa Rica	1	0.00418
Côte d’Ivoire	1	0.00418
Croatia	1	0.00418
Cuba	1	0.00418
Ecuador	1	0.00418
Eritrea	1	0.00418
Hong Kong	1	0.00418
Hungary	1	0.00418
Isle of Man	1	0.00418
Jamaica	1	0.00418
Jordan	1	0.00418
Kuwait	1	0.00418
Malta	1	0.00418
Morocco	1	0.00418
Nigeria	1	0.00418
Panama	1	0.00418
Qatar	1	0.00418
Russia	1	0.00418
Rwanda	1	0.00418
Serbia	1	0.00418
Sierra Leone	1	0.00418
Slovakia	1	0.00418
Slovenia	1	0.00418
Somalia	1	0.00418
South Korea	1	0.00418
Sri Lanka	1	0.00418
Uganda	1	0.00418
Ukraine	1	0.00418
United Arab Emirates	1	0.00418
Uruguay	1	0.00418
Vietnam	1	0.00418

======= geom_bar(stat="identity") + labs(title = "Age distribution of Ask a Manager survey respondents", x="Age group", y="Frequency", caption = "Source:Askamanager.com") # 'country' ask_a_manager_2021 %>% count(country, sort=TRUE) %>% mutate(percent = 100* n/sum(n)) >>>>>>> XuejingHuang-master # 'city' ask_a_manager_2021 %>% count(city, sort=TRUE) %>% <<<<<<< HEAD mutate(percent = 100* n/sum(n))

city	n	percent
Boston	726	3.03
Chicago	710	2.97
New York	650	2.72
Seattle	608	2.54
London	548	2.29
New York City	432	1.81
Los Angeles	429	1.79
San Francisco	409	1.71
Portland	398	1.66
Toronto	374	1.56
Minneapolis	333	1.39
Washington	330	1.38
Austin	304	1.27
Philadelphia	303	1.27
Atlanta	294	1.23
Denver	279	1.17
Washington, DC	233	0.974
Houston	226	0.944
Vancouver	205	0.857
Pittsburgh	202	0.844
Dallas	199	0.832
NYC	182	0.761
Cambridge	181	0.756
Madison	175	0.731
Columbus	173	0.723
San Diego	163	0.681
Baltimore	148	0.618
Indianapolis	147	0.614
Washington DC	142	0.593
Raleigh	136	0.568
Cleveland	134	0.56
Ottawa	130	0.543
Richmond	128	0.535
Phoenix	121	0.506
Remote	114	0.476
Arlington	109	0.456
Durham	106	0.443
Nashville	103	0.43
St. Louis	102	0.426
Cincinnati	101	0.422
DC	100	0.418
Sacramento	97	0.405
Melbourne	96	0.401
Milwaukee	95	0.397
Charlotte	92	0.384
Detroit	90	0.376
Manchester	88	0.368
Oakland	85	0.355
Salt Lake City	85	0.355
Rochester	83	0.347
Kansas City	79	0.33
Orlando	79	0.33
San Jose	79	0.33
Louisville	78	0.326
Sydney	78	0.326
Alexandria	74	0.309
Dublin	74	0.309
Ann Arbor	72	0.301
Montreal	69	0.288
Calgary	68	0.284
Tucson	68	0.284
Boulder	66	0.276
Des Moines	65	0.272
Tampa	65	0.272
Columbia	64	0.267
Birmingham	63	0.263
Omaha	62	0.259
Edmonton	61	0.255
St. Paul	60	0.251
Albuquerque	55	0.23
San Antonio	55	0.23
New Orleans	54	0.226
Albany	53	0.221
Springfield	53	0.221
Brooklyn	52	0.217
Grand Rapids	52	0.217
Lexington	52	0.217
Buffalo	49	0.205
Irvine	48	0.201
Jacksonville	48	0.201
Las Vegas	46	0.192
Memphis	46	0.192
Rockville	46	0.192
Hartford	45	0.188
Winnipeg	45	0.188
Berlin	44	0.184
Edinburgh	44	0.184
Bristol	43	0.18
Brisbane	42	0.176
Oklahoma City	42	0.176
Saint Paul	42	0.176
Boise	41	0.171
Burlington	41	0.171
Halifax	41	0.171
Wilmington	41	0.171
Bloomington	40	0.167
Fort Worth	40	0.167
Wellington	40	0.167
Bellevue	39	0.163
N/A	39	0.163
Leeds	38	0.159
Bethesda	37	0.155
Oxford	37	0.155
Redmond	37	0.155
Amsterdam	36	0.15
Colorado Springs	36	0.15
Providence	36	0.15
Syracuse	36	0.15
Tulsa	36	0.15
Wichita	36	0.15
Harrisburg	35	0.146
Charleston	34	0.142
Fort Collins	34	0.142
Charlottesville	33	0.138
Anchorage	32	0.134
Fairfax	32	0.134
Bay Area	31	0.13
Glasgow	31	0.13
Huntsville	31	0.13
Paris	31	0.13
Auckland	30	0.125
Lansing	30	0.125
Tacoma	30	0.125
Washington, D.C.	30	0.125
Dayton	29	0.121
Newark	29	0.121
Salem	29	0.121
Knoxville	28	0.117
Miami	28	0.117
Saint Louis	28	0.117
Silver Spring	28	0.117
Athens	27	0.113
Eugene	27	0.113
Greensboro	27	0.113
Lincoln	27	0.113
Reston	27	0.113
Victoria	27	0.113
Munich	26	0.109
Perth	26	0.109
Adelaide	25	0.104
Chapel Hill	25	0.104
Berkeley	24	0.1
Missoula	24	0.1
New Haven	24	0.1
Princeton	24	0.1
Gainesville	23	0.0961
Greenville	23	0.0961
Spokane	23	0.0961
Olympia	22	0.0919
Santa Clara	22	0.0919
Palo Alto	21	0.0878
Toledo	21	0.0878
Vienna	21	0.0878
Waltham	21	0.0878
Annapolis	20	0.0836
Canberra	20	0.0836
Chattanooga	20	0.0836
District of Columbia	20	0.0836
St Louis	20	0.0836
Troy	20	0.0836
Worcester	20	0.0836
	20	0.0836
Brussels	19	0.0794
Lafayette	19	0.0794
Little Rock	19	0.0794
Nottingham	19	0.0794
Scottsdale	19	0.0794
South Bend	19	0.0794
Baton Rouge	18	0.0752
Brighton	18	0.0752
Long Beach	18	0.0752
Manhattan	18	0.0752
McLean	18	0.0752
Mountain View	18	0.0752
Norfolk	18	0.0752
Overland Park	18	0.0752
Plano	18	0.0752
Prefer not to answer	18	0.0752
York	18	0.0752
Augusta	17	0.071
College Park	17	0.071
Frederick	17	0.071
Iowa City	17	0.071
Lancaster	17	0.071
Sunnyvale	17	0.071
Barcelona	16	0.0669
Champaign	16	0.0669
Concord	16	0.0669
Everett	16	0.0669
Fresno	16	0.0669
Honolulu	16	0.0669
Savannah	16	0.0669
Sheffield	16	0.0669
Tallahassee	16	0.0669
Tempe	16	0.0669
Waterloo	16	0.0669
Akron	15	0.0627
Reno	15	0.0627
Santa Cruz	15	0.0627
Saskatoon	15	0.0627
Allentown	14	0.0585
Beaverton	14	0.0585
Fort Lauderdale	14	0.0585
Hamilton	14	0.0585
Hillsboro	14	0.0585
Jackson	14	0.0585
Kitchener	14	0.0585
Liverpool	14	0.0585
Lowell	14	0.0585
Newton	14	0.0585
Reading	14	0.0585
San Francisco Bay Area	14	0.0585
San Mateo	14	0.0585
St Paul	14	0.0585
Alpharetta	13	0.0543
Asheville	13	0.0543
Bellingham	13	0.0543
Bethlehem	13	0.0543
Canton	13	0.0543
Fayetteville	13	0.0543
Fort Wayne	13	0.0543
Gaithersburg	13	0.0543
Hamburg	13	0.0543
Kalamazoo	13	0.0543
King of Prussia	13	0.0543
n/a	13	0.0543
new york	13	0.0543
Orange	13	0.0543
Pensacola	13	0.0543
Plymouth	13	0.0543
Regina	13	0.0543
Southampton	13	0.0543
Vancouver, BC	13	0.0543
Washington D.C.	13	0.0543
Aberdeen	12	0.0501
Aurora	12	0.0501
Cambridge, MA	12	0.0501
Cary	12	0.0501
Christchurch	12	0.0501
East Lansing	12	0.0501
Ithaca	12	0.0501
Lakewood	12	0.0501
Lehi	12	0.0501
Medford	12	0.0501
Morristown	12	0.0501
New york	12	0.0501
Newcastle	12	0.0501
Northampton	12	0.0501
Pasadena	12	0.0501
portland	12	0.0501
Portsmouth	12	0.0501
Prefer not to say	12	0.0501
SF Bay Area	12	0.0501
Stamford	12	0.0501
Belfast	11	0.046
Chicago suburbs	11	0.046
Cork	11	0.046
Corvallis	11	0.046
Evanston	11	0.046
Falls Church	11	0.046
Fargo	11	0.046
Frisco	11	0.046
Green Bay	11	0.046
Hanover	11	0.046
Herndon	11	0.046
Idaho Falls	11	0.046
Jersey City	11	0.046
Nashua	11	0.046
Ontario	11	0.046
Richland	11	0.046
Santa Fe	11	0.046
Santa Rosa	11	0.046
Sarasota	11	0.046
Twin Cities	11	0.046
Windsor	11	0.046
Andover	10	0.0418
Appleton	10	0.0418
Auburn	10	0.0418
Bentonville	10	0.0418
Boston, MA	10	0.0418
Broomfield	10	0.0418
College Station	10	0.0418
Denton	10	0.0418
DFW	10	0.0418
Fairbanks	10	0.0418
Irving	10	0.0418
La Crosse	10	0.0418
Middletown	10	0.0418
Needham, MA	10	0.0418
Norwich	10	0.0418
Poughkeepsie	10	0.0418
prefer not to answer	10	0.0418
Renton	10	0.0418
Rockford	10	0.0418
Scranton	10	0.0418
Somerset	10	0.0418
South San Francisco	10	0.0418
Southfield	10	0.0418
Topeka	10	0.0418
Utrecht	10	0.0418
Williamsburg	10	0.0418
Winston-Salem	10	0.0418
Ames	9	0.0376
Cedar Rapids	9	0.0376
Chandler	9	0.0376
D.C.	9	0.0376
Kent	9	0.0376
Lawrence	9	0.0376
Madrid	9	0.0376
Manassas	9	0.0376
Morgantown	9	0.0376
Naperville	9	0.0376
Newport	9	0.0376
Orange County	9	0.0376
Orem	9	0.0376
Parsippany	9	0.0376
Portland, OR	9	0.0376
Redwood City	9	0.0376
remote	9	0.0376
Schaumburg	9	0.0376
Somerville	9	0.0376
Trenton	9	0.0376
Waco	9	0.0376
Watertown	9	0.0376
West Palm Beach	9	0.0376
Bangor	8	0.0334
Bath	8	0.0334
Boston area	8	0.0334
Bothell	8	0.0334
Bozeman	8	0.0334
Cardiff	8	0.0334
chicago	8	0.0334
Chicago Suburbs	8	0.0334
Cupertino	8	0.0334
Davis	8	0.0334
Duluth	8	0.0334
Edmonton, Alberta	8	0.0334
Exeter	8	0.0334
Fairfield	8	0.0334
Gloucester	8	0.0334
Golden	8	0.0334
Guelph	8	0.0334
Leicester	8	0.0334
Lewiston	8	0.0334
Long Island	8	0.0334
Metro Detroit	8	0.0334
Milton Keynes	8	0.0334
Mobile	8	0.0334
Montgomery	8	0.0334
Na	8	0.0334
Newcastle upon Tyne	8	0.0334
Peoria	8	0.0334
Philadelphia suburbs	8	0.0334
Provo	8	0.0334
Redding	8	0.0334
Sioux Falls	8	0.0334
Spartanburg	8	0.0334
St. Petersburg	8	0.0334
Surrey	8	0.0334
Walnut Creek	8	0.0334
Woburn	8	0.0334
-	7	0.0293
Amherst	7	0.0293
Boca Raton	7	0.0293
Carson City	7	0.0293
Coventry	7	0.0293
Decatur	7	0.0293
Dubuque	7	0.0293
Düsseldorf	7	0.0293
Englewood	7	0.0293
Florence	7	0.0293
Fort Myers	7	0.0293
Frankfurt	7	0.0293
Franklin	7	0.0293
Glendale	7	0.0293
Helena	7	0.0293
Helsinki	7	0.0293
Kingston	7	0.0293
Laramie	7	0.0293
Lenexa	7	0.0293
Los angeles	7	0.0293
Mclean	7	0.0293
Midland	7	0.0293
Modesto	7	0.0293
Moline	7	0.0293
Morrisville	7	0.0293
N/a	7	0.0293
New York, NY	7	0.0293
Norman	7	0.0293
Ottawa, Ontario	7	0.0293
Richardson	7	0.0293
Riverside	7	0.0293
Roanoke	7	0.0293
Rural	7	0.0293
san francisco	7	0.0293
Suitland	7	0.0293
Toronto, Ontario	7	0.0293
Tuscaloosa	7	0.0293
White Plains	7	0.0293
Amarillo	6	0.0251
Anaheim	6	0.0251
Bedford	6	0.0251
Binghamton	6	0.0251
Blacksburg	6	0.0251
Carrollton	6	0.0251
Chantilly	6	0.0251
Chelmsford	6	0.0251
Chester	6	0.0251
Costa Mesa	6	0.0251
Draper	6	0.0251
El Paso	6	0.0251
Erie	6	0.0251
Framingham	6	0.0251
Greenbelt	6	0.0251
Harrisonburg	6	0.0251
Henderson	6	0.0251
Laurel	6	0.0251
Lebanon	6	0.0251
Leesburg	6	0.0251
Logan	6	0.0251
Lynchburg	6	0.0251
Marietta	6	0.0251
Natick	6	0.0251
Needham	6	0.0251
Novi	6	0.0251
Nyc	6	0.0251
Ogden	6	0.0251
Pocatello	6	0.0251
prefer not to say	6	0.0251
Pullman	6	0.0251
Quincy	6	0.0251
Saginaw	6	0.0251
San Marcos	6	0.0251
Santa Barbara	6	0.0251
seattle	6	0.0251
Seattle, WA	6	0.0251
SF	6	0.0251
Shelton	6	0.0251
Towson	6	0.0251
Tyler	6	0.0251
Various	6	0.0251
Verona	6	0.0251
Virginia Beach	6	0.0251
Warwick	6	0.0251
West Lafayette	6	0.0251
Woodstock	6	0.0251
Alameda	5	0.0209
Ashland	5	0.0209
Atlanta metro area	5	0.0209
Beaumont	5	0.0209
Billings	5	0.0209
boston	5	0.0209
Boston Area	5	0.0209
Broken Arrow	5	0.0209
Burbank	5	0.0209
Carlsbad	5	0.0209
Cherry Hill	5	0.0209
Chesterfield	5	0.0209
Chico	5	0.0209
Claremont	5	0.0209
Corpus Christi	5	0.0209
Daytona Beach	5	0.0209
Eagan	5	0.0209
Escondido	5	0.0209
Fredericksburg	5	0.0209
Fremont	5	0.0209
Grand Forks	5	0.0209
Greater Toronto Area	5	0.0209
Hampton	5	0.0209
Holland	5	0.0209
Home	5	0.0209
Horsham	5	0.0209
indianapolis	5	0.0209
Juneau	5	0.0209
Kennewick	5	0.0209
Kenosha	5	0.0209
Kirkland	5	0.0209
Lakeland	5	0.0209
Leawood	5	0.0209
Lisbon	5	0.0209
London, UK	5	0.0209
Lyon	5	0.0209
Mankato	5	0.0209
Marlborough	5	0.0209
Mason	5	0.0209
Menlo Park	5	0.0209
Milan	5	0.0209
Milford	5	0.0209
Murfreesboro	5	0.0209
New Brunswick	5	0.0209
New York city	5	0.0209
Northfield	5	0.0209
Norwalk	5	0.0209
Pleasanton	5	0.0209
Rock Hill	5	0.0209
Roseville	5	0.0209
San Luis Obispo	5	0.0209
Sanford	5	0.0209
Santa Ana	5	0.0209
Santa Monica	5	0.0209
Shreveport	5	0.0209
Small town	5	0.0209
St. Louis, MO	5	0.0209
Stafford	5	0.0209
State College	5	0.0209
Traverse City	5	0.0209
Utica	5	0.0209
Ventura	5	0.0209
washington	5	0.0209
West Chester	5	0.0209
Ypsilanti	5	0.0209
Aiken	4	0.0167
Aliso Viejo	4	0.0167
Batavia	4	0.0167
Bay City	4	0.0167
Blaine	4	0.0167
Bournemouth	4	0.0167
Bowling Green	4	0.0167
Bradenton	4	0.0167
Bremerton	4	0.0167
Brentwood	4	0.0167
Bronx	4	0.0167
Canada	4	0.0167
Chicago area	4	0.0167
Chicago, IL	4	0.0167
Chicagoland	4	0.0167
Clemson	4	0.0167
Cologne	4	0.0167
Conshohocken	4	0.0167
Copenhagen	4	0.0167
Danbury	4	0.0167
Dc	4	0.0167
Dover	4	0.0167
Downers Grove	4	0.0167
Easton	4	0.0167
Eau Claire	4	0.0167
Eden Prairie	4	0.0167
Edmonds	4	0.0167
Elizabethtown	4	0.0167
Evansville	4	0.0167
Exton	4	0.0167
Farmington Hills	4	0.0167
Flagstaff	4	0.0167
Florham Park	4	0.0167
Folsom	4	0.0167
Frankfort	4	0.0167
Galway	4	0.0167
Georgetown	4	0.0167
Gilbert	4	0.0167
Greeley	4	0.0167
Greenfield	4	0.0167
GTA	4	0.0167
Hamilton, Ontario	4	0.0167
Hoboken	4	0.0167
Issaquah	4	0.0167
Jonesboro	4	0.0167
Joplin	4	0.0167
Katy	4	0.0167
Keene	4	0.0167
Kelowna	4	0.0167
Las Cruces	4	0.0167
Lawrenceville	4	0.0167
Lisle	4	0.0167
Longmont	4	0.0167
Longview	4	0.0167
Los Alamos	4	0.0167
los angeles	4	0.0167
Marion	4	0.0167
Meridian	4	0.0167
Metairie	4	0.0167
Milwaukee area	4	0.0167
Minnetonka	4	0.0167
Mississauga	4	0.0167
Moncton	4	0.0167
Montgomery County	4	0.0167
Montpelier	4	0.0167
Montréal	4	0.0167
Moorhead	4	0.0167
na	4	0.0167
Nanaimo	4	0.0167
Newport News	4	0.0167
Niles	4	0.0167
North Chicago	4	0.0167
Northbrook	4	0.0167
Oak Ridge	4	0.0167
Olathe	4	0.0167
phoenix	4	0.0167
Pomona	4	0.0167
Port Orchard	4	0.0167
Portland, ME	4	0.0167
Puyallup	4	0.0167
Rapid City	4	0.0167
Regional	4	0.0167
Research Triangle Park	4	0.0167
rural	4	0.0167
Rural area	4	0.0167
Rural Ontario	4	0.0167
Salina	4	0.0167
Salt lake city	4	0.0167
San francisco	4	0.0167
San Rafael	4	0.0167
San Ramon	4	0.0167
Sandy	4	0.0167
Saratoga	4	0.0167
Schenectady	4	0.0167
South Jordan	4	0.0167
St. John's	4	0.0167
Stratford	4	0.0167
Terre Haute	4	0.0167
Thousand Oaks	4	0.0167
toronto	4	0.0167
Torrance	4	0.0167
Tualatin	4	0.0167
Tupelo	4	0.0167
Tustin	4	0.0167
Tysons	4	0.0167
UK	4	0.0167
Upstate NY	4	0.0167
Urbana	4	0.0167
Vancouver, British Columbia	4	0.0167
Warner Robins	4	0.0167
Warren	4	0.0167
washington dc	4	0.0167
Waukesha	4	0.0167
Waynesboro	4	0.0167
West Des Moines	4	0.0167
Westlake	4	0.0167
Westminster	4	0.0167
WFH	4	0.0167
Winston Salem	4	0.0167
Work from home	4	0.0167
Yuma	4	0.0167
Abbotsford	3	0.0125
Adelaide, South Australia	3	0.0125
Albany, NY	3	0.0125
Alberta	3	0.0125
Alexandria, VA	3	0.0125
Anderson	3	0.0125
Ankeny	3	0.0125
Anonymous	3	0.0125
Antwerp	3	0.0125
Arlington Heights	3	0.0125
Arlington, VA	3	0.0125
Arvada	3	0.0125
Ashburn	3	0.0125
atlanta	3	0.0125
Atlantic City	3	0.0125
Attleboro	3	0.0125
Austin, TX	3	0.0125
Bakersfield	3	0.0125
Barnstable	3	0.0125
BC	3	0.0125
Beachwood	3	0.0125
Bemidji	3	0.0125
Bend	3	0.0125
Bennington	3	0.0125
Bergen County	3	0.0125
Birmingham, AL	3	0.0125
Bismarck	3	0.0125
Bloomfield Hills	3	0.0125
Bonn	3	0.0125
Bradford	3	0.0125
Bridgeport	3	0.0125
Bridgewater	3	0.0125
British Columbia	3	0.0125
Brockville	3	0.0125
Brookline	3	0.0125
Brunswick	3	0.0125
Camden	3	0.0125
Cape Girardeau	3	0.0125
Carbondale	3	0.0125
Carlisle	3	0.0125
Central PA	3	0.0125
Chanhassen	3	0.0125
charlotte	3	0.0125
Charlottetown	3	0.0125
Cheltenham	3	0.0125
Chesapeake	3	0.0125
Cheshire	3	0.0125
Cheyenne	3	0.0125
Chilliwack	3	0.0125
Cincinnati, OH	3	0.0125
City	3	0.0125
Clinton	3	0.0125
Colorado springs	3	0.0125
Columbus, OH	3	0.0125
Council Bluffs	3	0.0125
Cumming	3	0.0125
Dearborn	3	0.0125
Denver metro	3	0.0125
Denver Metro Area	3	0.0125
Denver, CO	3	0.0125
Douglasville	3	0.0125
Doylestown	3	0.0125
dublin	3	0.0125
Dulles	3	0.0125
East Hanover	3	0.0125
Elizabeth	3	0.0125
Eureka	3	0.0125
Fairfax County	3	0.0125
Fall River	3	0.0125
Fishers	3	0.0125
Fort Smith	3	0.0125
Fort wayne	3	0.0125
Fredericton	3	0.0125
Freeport	3	0.0125
Fullerton	3	0.0125
Geelong	3	0.0125
Germany	3	0.0125
Glendale Heights	3	0.0125
Glens Falls	3	0.0125
Grand Junction	3	0.0125
Greencastle	3	0.0125
Greenwich	3	0.0125
Gulfport	3	0.0125
Hackensack	3	0.0125
Halifax, Nova Scotia	3	0.0125
Hammond	3	0.0125
Hampton Roads	3	0.0125
hartford	3	0.0125
Hatfield	3	0.0125
Hattiesburg	3	0.0125
Hendersonville	3	0.0125
Hickory	3	0.0125
Hudson	3	0.0125
Hull	3	0.0125
Itasca	3	0.0125
Jefferson City	3	0.0125
Jeffersonville	3	0.0125
Johnston	3	0.0125
Joliet	3	0.0125
Kingsport	3	0.0125
Kitchener, Ontario	3	0.0125
LA	3	0.0125
La Grande	3	0.0125
Lake Forest	3	0.0125
Lansing, MI	3	0.0125
Largo	3	0.0125
Leeds, UK	3	0.0125
Lemont	3	0.0125
London, Ontario	3	0.0125
Loveland	3	0.0125
Lubbock	3	0.0125
Lynnwood	3	0.0125
Mahwah	3	0.0125
Malvern	3	0.0125
Many	3	0.0125
Maplewood	3	0.0125
Marquette	3	0.0125
Martinsburg	3	0.0125
Mechanicsburg	3	0.0125
Medina	3	0.0125
Melville	3	0.0125
Menomonee Falls	3	0.0125
Merced	3	0.0125
Metro Atlanta	3	0.0125
Middleton	3	0.0125
Minneapolis-St. Paul	3	0.0125
Minneapolis/St Paul	3	0.0125
Monroe	3	0.0125
Montvale	3	0.0125
Mount Laurel	3	0.0125
Mount Vernon	3	0.0125
Naples	3	0.0125
Nassau	3	0.0125
New Castle	3	0.0125
Newburyport	3	0.0125
No	3	0.0125
Normal	3	0.0125
North Andover	3	0.0125
Northern Ontario	3	0.0125
Northern VA	3	0.0125
Novato	3	0.0125
NY	3	0.0125
Oak Brook	3	0.0125
Olney	3	0.0125
Oshkosh	3	0.0125
Owings Mills	3	0.0125
Palm Springs	3	0.0125
Paso Robles	3	0.0125
Perrysburg	3	0.0125
Petaluma	3	0.0125
Peterborough	3	0.0125
Piscataway	3	0.0125
pittsburgh	3	0.0125
Plattsburgh	3	0.0125
Plymouth Meeting	3	0.0125
Portland, Oregon	3	0.0125
Prince George	3	0.0125
Pueblo	3	0.0125
Quantico	3	0.0125
Quebec City	3	0.0125
Queens	3	0.0125
Racine	3	0.0125
Red Bank	3	0.0125
Redlands	3	0.0125
Remote worker	3	0.0125
Riverdale	3	0.0125
Rochester Hills	3	0.0125
Rochester, NY	3	0.0125
Rogers	3	0.0125
Rosslyn	3	0.0125
Roswell	3	0.0125
Rotterdam	3	0.0125
San antonio	3	0.0125
San diego	3	0.0125
Saratoga Springs	3	0.0125
Sheboygan	3	0.0125
Sierra Vista	3	0.0125
Silverdale	3	0.0125
Singapore	3	0.0125
Sioux falls	3	0.0125
Solon	3	0.0125
South Hadley	3	0.0125
St Paul, MN	3	0.0125
St. Cloud	3	0.0125
Stanford	3	0.0125
Starkville	3	0.0125
Staunton	3	0.0125
Sterling	3	0.0125
Stevens Point	3	0.0125
Stockholm	3	0.0125
Suburban Chicago	3	0.0125
Sunderland	3	0.0125
Suwanee	3	0.0125
Swindon	3	0.0125
Tarrytown	3	0.0125
Tel Aviv	3	0.0125
The Hague	3	0.0125
Thunder Bay	3	0.0125
Tigard	3	0.0125
Truro	3	0.0125
Valencia	3	0.0125
Valparaiso	3	0.0125
vancouver	3	0.0125
Vancouver Island	3	0.0125
Virginia	3	0.0125
Virtual	3	0.0125
Wakefield	3	0.0125
Waterford	3	0.0125
Waterville	3	0.0125
Weehawken	3	0.0125
Wells	3	0.0125
Westbrook	3	0.0125
Westchester	3	0.0125
Westfield	3	0.0125
Westwood	3	0.0125
Wheeling	3	0.0125
Whitehorse	3	0.0125
Winchester	3	0.0125
Woodinville	3	0.0125
--	2	0.00836
.	2	0.00836
A Coruña	2	0.00836
Abilene	2	0.00836
Accra	2	0.00836
Addison	2	0.00836
Agawam	2	0.00836
Alamogordo	2	0.00836
albuquerque	2	0.00836
Allen	2	0.00836
Alton	2	0.00836
Ambler	2	0.00836
amsterdam	2	0.00836
anchorage	2	0.00836
ann arbor	2	0.00836
Annandale	2	0.00836
Apache Junction	2	0.00836
Apex	2	0.00836
Apple Valley	2	0.00836
arlington	2	0.00836
Arroyo Grande	2	0.00836
Aspen	2	0.00836
ATLANTA	2	0.00836
Atlanta, GA	2	0.00836
austin	2	0.00836
Ayer	2	0.00836
Baltimore Area	2	0.00836
Baltimore County	2	0.00836
Baltimore, MD	2	0.00836
Bangalore	2	0.00836
Bangkok	2	0.00836
Barrie	2	0.00836
Barrington	2	0.00836
Basingstoke	2	0.00836
Batesville	2	0.00836
Bay area	2	0.00836
Belmont	2	0.00836
Berwyn	2	0.00836
Bethesda MD	2	0.00836
Beverly Hills	2	0.00836
Birmingham UK	2	0.00836
Birmingham, England	2	0.00836
Birmingham, UK	2	0.00836
Bishop	2	0.00836
Bloomfield	2	0.00836
Blue Bell	2	0.00836
Bluffton	2	0.00836
BOSTON	2	0.00836
Boston suburbs	2	0.00836
Bowmanville	2	0.00836
Boynton Beach	2	0.00836
Braintree	2	0.00836
Brampton	2	0.00836
Branford	2	0.00836
Brantford	2	0.00836
Brattleboro	2	0.00836
Braunschweig	2	0.00836
Brewster	2	0.00836
Brookfield	2	0.00836
Buckinghamshire	2	0.00836
buffalo	2	0.00836
Burlingame	2	0.00836
Byron Bay	2	0.00836
Calgary, AB	2	0.00836
Calgary, Alberta	2	0.00836
Cambridge, ON	2	0.00836
Cambridge, UK	2	0.00836
Camp hill	2	0.00836
Campbell	2	0.00836
Cape Town	2	0.00836
Carmel	2	0.00836
Carpinteria	2	0.00836
Carson	2	0.00836
Cary, NC	2	0.00836
Casper	2	0.00836
Centennial	2	0.00836
Central IL	2	0.00836
Central Virginia	2	0.00836
Chestnut Hill	2	0.00836
Chevy Chase	2	0.00836
Chicago (suburbs)	2	0.00836
Chicopee	2	0.00836
Clarksburg	2	0.00836
Cleveland, Ohio	2	0.00836
Cloquet	2	0.00836
Cluj-Napoca	2	0.00836
Coeur d'Alene	2	0.00836
Collegeville	2	0.00836
Collingswood	2	0.00836
Columbia, SC	2	0.00836
Conyers	2	0.00836
Coral Gables	2	0.00836
Coralville	2	0.00836
Covington	2	0.00836
Crawley	2	0.00836
Crossville	2	0.00836
Croydon	2	0.00836
Culver City	2	0.00836
Curitiba	2	0.00836
dallas	2	0.00836
Dalton	2	0.00836
Danvers	2	0.00836
Danville	2	0.00836
dc	2	0.00836
DC metro area	2	0.00836
Decline	2	0.00836
Decline to answer	2	0.00836
Deerfield Beach	2	0.00836
DeKalb	2	0.00836
Delaware	2	0.00836
Delft	2	0.00836
Denver CO	2	0.00836
Des Moines metro	2	0.00836
Detroit area	2	0.00836
Detroit metro	2	0.00836
Detroit Metro	2	0.00836
Detroit, MI	2	0.00836
Devon	2	0.00836
Dunedin	2	0.00836
Durango	2	0.00836
East Hartford	2	0.00836
East Moline	2	0.00836
East Windsor	2	0.00836
Eastern Iowa	2	0.00836
Eastern Washington	2	0.00836
Edison	2	0.00836
edmonton	2	0.00836
Effingham	2	0.00836
Elkhart	2	0.00836
Elkridge	2	0.00836
Ellicott City	2	0.00836
Elmira	2	0.00836
Elyria	2	0.00836
Emeryville	2	0.00836
Essex	2	0.00836
Eufaula	2	0.00836
Evanston, IL	2	0.00836
Fair Lawn	2	0.00836
Fallon	2	0.00836
Falls Church, VA	2	0.00836
Falmouth	2	0.00836
Farmington	2	0.00836
Flemington	2	0.00836
Fort Bragg	2	0.00836
Fort McMurray	2	0.00836
Fort Washington	2	0.00836
Framingham, MA	2	0.00836
Fredonia	2	0.00836
From home	2	0.00836
Ft Lauderdale	2	0.00836
Ft Meade	2	0.00836
Fully Remote	2	0.00836
Fulton	2	0.00836
Galveston	2	0.00836
Garden City	2	0.00836
Garden grove	2	0.00836
Garyville	2	0.00836
Gent	2	0.00836
Gold Coast	2	0.00836
Grand rapids	2	0.00836
Grapevine	2	0.00836
Greater Boston	2	0.00836
Greater Boston area	2	0.00836
Greater Boston Area	2	0.00836
Greater Chicago Area	2	0.00836
Greater Cleveland	2	0.00836
Greater London	2	0.00836
Greater Philadelphia area	2	0.00836
Greater Philly	2	0.00836
Grinnell	2	0.00836
Groton	2	0.00836
Grove City	2	0.00836
Guildford	2	0.00836
Hagerstown	2	0.00836
Halifax, NS	2	0.00836
Hamden	2	0.00836
Hamilton, ON	2	0.00836
Havre de Grace	2	0.00836
Hawthorne	2	0.00836
Hayward	2	0.00836
Heilbronn	2	0.00836
Herne	2	0.00836
High Point	2	0.00836
Hillsborough	2	0.00836
Hilo	2	0.00836
Hollywood	2	0.00836
Houston area	2	0.00836
Houston, TX	2	0.00836
Huddersfield	2	0.00836
Hunt Valley	2	0.00836
Huntingdon	2	0.00836
Huntington	2	0.00836
Ipswich	2	0.00836
Ireland	2	0.00836
Iselin	2	0.00836
Jamestown	2	0.00836
Jefferson	2	0.00836
Johnson City	2	0.00836
Jupiter	2	0.00836
Kansas city	2	0.00836
Kansas City area	2	0.00836
Karachi	2	0.00836
Kearney	2	0.00836
Kelowna, BC	2	0.00836
Kennebunk	2	0.00836
Kennesaw	2	0.00836
Kensington	2	0.00836
Kirtland	2	0.00836
Kitchener, ON	2	0.00836
La Vergne	2	0.00836
Lake Bluff	2	0.00836
Lake Jackson	2	0.00836
Lake Mary	2	0.00836
Lake Zurich	2	0.00836
Lansdale	2	0.00836
Larkspur	2	0.00836
Launceston	2	0.00836
Lawrenceville, NJ	2	0.00836
Leamington Spa	2	0.00836
Lee's Summit	2	0.00836
Lehigh Valley	2	0.00836
Leiden	2	0.00836
Leominster	2	0.00836
Leuven	2	0.00836
Lewisville	2	0.00836
Lima	2	0.00836
Lititz	2	0.00836
Livermore	2	0.00836
Livingston	2	0.00836
Livonia	2	0.00836
Los Angeles County	2	0.00836
Los Angeles, CA	2	0.00836
Loughborough	2	0.00836
louisville	2	0.00836
Lower Mainland	2	0.00836
Luton	2	0.00836
Luxembourg	2	0.00836
Mackay	2	0.00836
Malone	2	0.00836
Manchester, UK	2	0.00836
Manila	2	0.00836
Maple Grove	2	0.00836
Markham	2	0.00836
Marlton	2	0.00836
Marshfield	2	0.00836
Maryland	2	0.00836
Marysville	2	0.00836
Maryville	2	0.00836
McDonough	2	0.00836
McLean, VA	2	0.00836
McMinnville	2	0.00836
Mechanicsville	2	0.00836
Media	2	0.00836
melbourne	2	0.00836
Merrimack	2	0.00836
Merritt Island	2	0.00836
Mesa	2	0.00836
Mexico City	2	0.00836
Miami Beach	2	0.00836
Milpitas	2	0.00836
milwaukee	2	0.00836
Milwaukee, WI	2	0.00836
minneapolis	2	0.00836
Minneapolis suburbs	2	0.00836
Minneapolis-St Paul	2	0.00836
Mishawaka	2	0.00836
Mission	2	0.00836
Monterey	2	0.00836
Montreal, QC	2	0.00836
Morgan Hill	2	0.00836
Morris	2	0.00836
Moscow	2	0.00836
Muncie	2	0.00836
Murray	2	0.00836
Nairobi	2	0.00836
Napa	2	0.00836
nashville	2	0.00836
Nauvoo	2	0.00836
Near LA	2	0.00836
Neenah	2	0.00836
Nevada	2	0.00836
New Albany	2	0.00836
New Britain	2	0.00836
New haven	2	0.00836
New Hope	2	0.00836
New Jersey	2	0.00836
New London	2	0.00836
New Paltz	2	0.00836
New Plymouth	2	0.00836
New Ulm	2	0.00836
Newburgh	2	0.00836
Newcastle NSW	2	0.00836
Newcastle Upon Tyne	2	0.00836
Newcastle-upon-Tyne	2	0.00836
Newnan	2	0.00836
Newtown	2	0.00836
Newtown Square	2	0.00836
None	2	0.00836
Norcross	2	0.00836
North Bay	2	0.00836
North Carolina	2	0.00836
North East	2	0.00836
North Haven	2	0.00836
North of Boston	2	0.00836
North Port	2	0.00836
Northeast Ohio	2	0.00836
Nova Scotia	2	0.00836
Nuremberg	2	0.00836
Nürnberg	2	0.00836
O	2	0.00836
O'Fallon	2	0.00836
Oakville	2	0.00836
Ocean Springs	2	0.00836
Oceanside	2	0.00836
Okinawa	2	0.00836
Omaha, NE	2	0.00836
Opelika	2	0.00836
Orange County Area	2	0.00836
orlando	2	0.00836
Other	2	0.00836
ottawa	2	0.00836
Owensboro	2	0.00836
Oxfordshire	2	0.00836
Palmdale	2	0.00836
Palo alto	2	0.00836
Paramus	2	0.00836
Paterson	2	0.00836
Pelham	2	0.00836
Pembroke	2	0.00836
Peoria Illinois	2	0.00836
philadelphia	2	0.00836
Philadelphia suburb	2	0.00836
Phx	2	0.00836
Pittsboro	2	0.00836
Pittsburg	2	0.00836
PIttsburgh	2	0.00836
Pittsburgh, PA	2	0.00836
Pittsfield	2	0.00836
Plantation	2	0.00836
Platteville	2	0.00836
Portage	2	0.00836
Portland area	2	0.00836
Potomac	2	0.00836
Potsdam	2	0.00836
Pottstown	2	0.00836
Prefer not to disclose	2	0.00836
Prefer not to respond	2	0.00836
Prescott	2	0.00836
Preston	2	0.00836
princeton	2	0.00836
Province	2	0.00836
Provincetown	2	0.00836
Purchase	2	0.00836
Quakertown	2	0.00836
Queensland	2	0.00836
Raleigh-Durham	2	0.00836
Raleigh, NC	2	0.00836
Raymond	2	0.00836
Reading, UK	2	0.00836
Reims	2	0.00836
Remotely	2	0.00836
Remotely from MD	2	0.00836
Research Triangle	2	0.00836
Richmond, VA	2	0.00836
Ridgefield	2	0.00836
Riga	2	0.00836
Ripon	2	0.00836
Riverview	2	0.00836
Rockland	2	0.00836
Rockville Centre	2	0.00836
Rolla	2	0.00836
Rolling Meadows	2	0.00836
Rome	2	0.00836
Ronkonkoma	2	0.00836
Rosemont	2	0.00836
Rosemount	2	0.00836
Rural town	2	0.00836
Sacramento, CA	2	0.00836
Saint Cloud	2	0.00836
Saint paul	2	0.00836
Saint Petersburg	2	0.00836
Salem, OR	2	0.00836
Salinas	2	0.00836
Saline	2	0.00836
Salt Lake	2	0.00836
San Bernardino	2	0.00836
San Francisco Bay area	2	0.00836
SAN JOSE	2	0.00836
San Jose, CA	2	0.00836
San Juan	2	0.00836
Sandpoint	2	0.00836
Saranac Lake	2	0.00836
Seabrook	2	0.00836
Seattle area	2	0.00836
Seattle Area	2	0.00836
Simi Valley	2	0.00836
Slc	2	0.00836
Sligo	2	0.00836
small town	2	0.00836
Sofia	2	0.00836
South Burlington	2	0.00836
South East England	2	0.00836
South Jersey	2	0.00836
South Lake Tahoe	2	0.00836
South Sioux City	2	0.00836
Southern California	2	0.00836
St Albans	2	0.00836
St louis	2	0.00836
st paul	2	0.00836
St. Marys	2	0.00836
Stillwater	2	0.00836
Storrs	2	0.00836
Stuttgart	2	0.00836
Suburb of Chicago	2	0.00836
Suburbs of Philadelphia	2	0.00836
Sudbury	2	0.00836
Suffolk	2	0.00836
Summit	2	0.00836
Sunrise	2	0.00836
Sussex	2	0.00836
Swansea	2	0.00836
sydney	2	0.00836
Sydney, Australia	2	0.00836
Tallinn	2	0.00836
Texarkana	2	0.00836
The Woodlands	2	0.00836
Thessaloniki	2	0.00836
Titusville	2	0.00836
Tokyo	2	0.00836
Toronto, ON	2	0.00836
Torrington	2	0.00836
Townsville	2	0.00836
Tracy	2	0.00836
Triangle	2	0.00836
Truckee	2	0.00836
Trumbull	2	0.00836
Tumwater	2	0.00836
Tunbridge Wells	2	0.00836
Twin Cities Metro	2	0.00836
Twin Cities suburbs	2	0.00836
Twin Falls	2	0.00836
Ulm	2	0.00836
Undisclosed	2	0.00836
Upper Marlboro	2	0.00836
Utah County	2	0.00836
Vacaville	2	0.00836
Valdosta	2	0.00836
Vallejo	2	0.00836
Vancouver BC	2	0.00836
Vernon	2	0.00836
Vestal	2	0.00836
Vicksburg	2	0.00836
VICTORIA	2	0.00836
Victoria, BC	2	0.00836
Vilnius	2	0.00836
Walla Walla	2	0.00836
Warminster	2	0.00836
Warrenville	2	0.00836
Warsaw	2	0.00836
Washington, D.C	2	0.00836
Waterloo, Ontario	2	0.00836
Wayne	2	0.00836
West Hartford	2	0.00836
West palm beach	2	0.00836
Weymouth	2	0.00836
Whippany	2	0.00836
Whitewater	2	0.00836
Wilkes Barre	2	0.00836
Windsor Locks	2	0.00836
Winnetka	2	0.00836
Winter Haven	2	0.00836
Wyomissing	2	0.00836
xxx	2	0.00836
Yellowknife	2	0.00836
Zwolle	2	0.00836
---	1	0.00418
-----	1	0.00418
“Large” Canadian prairie city.	1	0.00418
(Province of Alberta)	1	0.00418
(Rural)	1	0.00418
**Too small a sample size, can't share	1	0.00418
/	1	0.00418
0000	1	0.00418
12043	1	0.00418
46901	1	0.00418
60k Small City	1	0.00418
A city in the UK	1	0.00418
A city small enough to not answer this question	1	0.00418
A large city	1	0.00418
A major Canadian city	1	0.00418
A major one	1	0.00418
A small city	1	0.00418
a small village in Okinawa prefecture (sorry, it's too identifying)	1	0.00418
A very large city	1	0.00418
Aachen	1	0.00418
Aberdeen Proving Ground	1	0.00418
Aberdeenshire	1	0.00418
Abidjan	1	0.00418
Abington	1	0.00418
Abington, PA	1	0.00418
Acton, MA	1	0.00418
Affluent Chicago suburb	1	0.00418
Ainsworth	1	0.00418
Akron, Ohio	1	0.00418
Albany region	1	0.00418
Albany-Schenectady-Troy Metro Area	1	0.00418
Albemarle	1	0.00418
Albuquerqur	1	0.00418
Aldie	1	0.00418
Aldie, VA	1	0.00418
Alexandria VA	1	0.00418
Alexandria, LA	1	0.00418
Alexandrira	1	0.00418
Algonac	1	0.00418
Alice Springs	1	0.00418
all over N CA	1	0.00418
Allegan	1	0.00418
Allen Park	1	0.00418
Allen, TX	1	0.00418
Allen/Plano	1	0.00418
Allendale, MI	1	0.00418
Alpharetta, GA	1	0.00418
Alsace (remote to Paris)	1	0.00418
Altadena	1	0.00418
Altamonte Springs	1	0.00418
Altoona	1	0.00418
Altus	1	0.00418
Alvarado	1	0.00418
American Fork	1	0.00418
Amesbury	1	0.00418
Amherst, MA	1	0.00418
Amityville	1	0.00418
Amman	1	0.00418
Ammon	1	0.00418
Amory	1	0.00418
Amsterdsm	1	0.00418
An exact answer would out me	1	0.00418
Anaconda	1	0.00418
Anacortes	1	0.00418
Anaheim-Santa Ana-Irvine metro region	1	0.00418
Anchoragr	1	0.00418
Ann Arbor area	1	0.00418
Ann Arbor, Michigan	1	0.00418
annandale Va	1	0.00418
Annapolis Junction	1	0.00418
Annapolis Valley	1	0.00418
Anon	1	0.00418
anonymous	1	0.00418
Arcadia	1	0.00418
Arden	1	0.00418
Ardmore	1	0.00418
Ardrossan	1	0.00418
Arkadelphia	1	0.00418
Arles	1	0.00418
ARLINGTON	1	0.00418
arlington, va	1	0.00418
Arlington, VA (Washington D.C. metro area)	1	0.00418
Arnold	1	0.00418
Asbury Park	1	0.00418
Asheboro	1	0.00418
Asheville, NC	1	0.00418
Ashford	1	0.00418
Asmara	1	0.00418
aston	1	0.00418
at home	1	0.00418
Atascadero	1	0.00418
Athlone	1	0.00418
Athol	1	0.00418
AtlantA	1	0.00418
Atlanta Metro	1	0.00418
Atlanta suburb	1	0.00418
Atlanta, Georgia	1	0.00418
Atlanta/Decatur	1	0.00418
Auburn Hills	1	0.00418
auckland	1	0.00418
Augusta - remote	1	0.00418
Augusta IL	1	0.00418
aurora	1	0.00418
Austell	1	0.00418
Austin metro area	1	0.00418
Austin Metro Area	1	0.00418
Australia not Sydney	1	0.00418
Avon Lake	1	0.00418
Aylesbury	1	0.00418
Badajoz	1	0.00418
Baden-Württemberg	1	0.00418
Bagdad	1	0.00418
Bainbridge Island	1	0.00418
Ball Ground	1	0.00418
Ballarat	1	0.00418
Ballston Spa	1	0.00418
BALTIMORE	1	0.00418
baltimore metro	1	0.00418
Baltimore metro region, MD	1	0.00418
Baltimore suburb	1	0.00418
Baltimore, Maryland	1	0.00418
Bangot	1	0.00418
Bannockburn	1	0.00418
Bardstown, KY. Store is located in Frankfort	1	0.00418
Barrie Ontario	1	0.00418
Bartlesville	1	0.00418
Bartlett	1	0.00418
barxelona	1	0.00418
Based on current client project	1	0.00418
Basel	1	0.00418
BASEL	1	0.00418
Basking Ridge	1	0.00418
Bathesda	1	0.00418
Bay Area / Palo Alto	1	0.00418
Bay Area, CA	1	0.00418
Bay of Plenty	1	0.00418
Bay St. Louis	1	0.00418
Beacon	1	0.00418
Beaufort	1	0.00418
Beaverton, OR (Portland suburb)	1	0.00418
Bedford, NH	1	0.00418
Bedfordshire	1	0.00418
Bedminster	1	0.00418
Beijing	1	0.00418
BELFAST	1	0.00418
Belfast - for context, our organization has country-based payscales, not city/state	1	0.00418
Belleville	1	0.00418
Bellevue, WA	1	0.00418
Bellevue, wa, usa	1	0.00418
Belmont, CA (SF Bay Area)	1	0.00418
Belo Horizonte	1	0.00418
Beltsville	1	0.00418
Bend, OR	1	0.00418
Bensenville	1	0.00418
Benton	1	0.00418
Benton Harbor	1	0.00418
Berea	1	0.00418
Bergen	1	0.00418
Berkley	1	0.00418
Berkley Heights	1	0.00418
berlin	1	0.00418
Berlin (remote for US clients)	1	0.00418
Berlin, Germany	1	0.00418
Berryville	1	0.00418
Berwick	1	0.00418
Bessemer	1	0.00418
Bethel	1	0.00418
Bethlehem, PA	1	0.00418
Bettendorf	1	0.00418
Beverly	1	0.00418
Beverly Hills, CA	1	0.00418
Biddeford	1	0.00418
BIG BEAR CITY	1	0.00418
Bigfork	1	0.00418
bilbao	1	0.00418
Billingham	1	0.00418
Biloxi	1	0.00418
Birm	1	0.00418
Blackpool	1	0.00418
Bloomington, IL	1	0.00418
Bluffdale	1	0.00418
Boardman	1	0.00418
Boca Chica	1	0.00418
Boerne	1	0.00418
Bolingbrook	1	0.00418
Bolton	1	0.00418
Bonita Springs	1	0.00418
Bordeaux	1	0.00418
Boron	1	0.00418
Boston (greater Boston metro area)	1	0.00418
Boston (Somerville specifically)	1	0.00418
Boston MA	1	0.00418
Boston Massachusetts	1	0.00418
Boston metro	1	0.00418
Boston Metro area	1	0.00418
Boston Metro Area	1	0.00418
Boston Suburb	1	0.00418
Boston, ma	1	0.00418
Boston(ish)	1	0.00418
Boston/Cambridge	1	0.00418
Boulder but we are remote	1	0.00418
Boulder City	1	0.00418
Boulder, CO	1	0.00418
Boxborough	1	0.00418
Boyertown	1	0.00418
Bozeman, MT	1	0.00418
Bracknell	1	0.00418
Braine	1	0.00418
Brambleton	1	0.00418
Brandon	1	0.00418
Bratislava	1	0.00418
Brea	1	0.00418
Bremen	1	0.00418
Bremerhaven	1	0.00418
Brest	1	0.00418
Brick	1	0.00418
Bridgend	1	0.00418
Bridgewater, NJ	1	0.00418
Brigham City	1	0.00418
brighton	1	0.00418
Brighton & Hove	1	0.00418
Brighton, MI	1	0.00418
Bristol UK	1	0.00418
Bristol, UK	1	0.00418
Bristow	1	0.00418
British Columbia (not a city, but I am rural)	1	0.00418
British Columbia (Province)	1	0.00418
Brittany	1	0.00418
brooklyn	1	0.00418
Brooklyn (WFH)	1	0.00418
Brooklyn Park	1	0.00418
Brooklyn Park, MN	1	0.00418
Brookston	1	0.00418
Brooksville	1	0.00418
Brookville	1	0.00418
Brownsville	1	0.00418
Brussel	1	0.00418
Bryan	1	0.00418
Bryan/College Station	1	0.00418
Bryn Mawr	1	0.00418
Bucharest	1	0.00418
Buckhannon	1	0.00418
Budapest	1	0.00418
Buenos Aires	1	0.00418
Buffalo Grove	1	0.00418
Buffalo, NY	1	0.00418
burbank	1	0.00418
Burgenland	1	0.00418
Burgos	1	0.00418
Burleson	1	0.00418
Burlington, MA	1	0.00418
Burly	1	0.00418
Burnaby	1	0.00418
Burnaby BC	1	0.00418
Burnsville, MN	1	0.00418
Butler	1	0.00418
BWG	1	0.00418
ca. 100k inhabitants	1	0.00418
Calgary AB	1	0.00418
Calgary Alberta	1	0.00418
Calgary Alberta Canada	1	0.00418
Calhoun, GA	1	0.00418
California	1	0.00418
Calumet City	1	0.00418
Camarillo	1	0.00418
Camarillo, CA	1	0.00418
Cambridge ,MA	1	0.00418
Cambridge (Boston metro area)	1	0.00418
Cambridge / Remote	1	0.00418
cambridge ma	1	0.00418
Cambridge MA	1	0.00418
Cambridge, Massachusetts	1	0.00418
Cambridge, Ontario	1	0.00418
Cambridge/Boston	1	0.00418
Cameron	1	0.00418
Camp Hill	1	0.00418
Camp Verde	1	0.00418
Campbell River	1	0.00418
can't say	1	0.00418
Can't say without being identifiable	1	0.00418
Can't say without losing my anonymity	1	0.00418
Can't share	1	0.00418
canberra	1	0.00418
Canbridge, UK	1	0.00418
Cannock	1	0.00418
canyon	1	0.00418
Cape Canaveral	1	0.00418
Capital Region	1	0.00418
Cardington	1	0.00418
Carlinville	1	0.00418
Carmel, Indiana	1	0.00418
Cartersville	1	0.00418
Cartersville GA	1	0.00418
Castlegar	1	0.00418
Caterham	1	0.00418
Catonsville	1	0.00418
CDA	1	0.00418
Cedar Grove, NJ	1	0.00418
cedar rapids	1	0.00418
Cedar rapids	1	0.00418
Cedarville	1	0.00418
Celina	1	0.00418
Center	1	0.00418
Centerville	1	0.00418
CENTRAL CITY	1	0.00418
Central Illinois	1	0.00418
Central Iowa	1	0.00418
Central Kentucky	1	0.00418
Central KY	1	0.00418
Central Maine	1	0.00418
Central MD	1	0.00418
central nj	1	0.00418
Central NJ	1	0.00418
Central NY	1	0.00418
Central Ohio	1	0.00418
Central Qld	1	0.00418
Central Valley	1	0.00418
Centralia	1	0.00418
Centreville	1	0.00418
Cerritos	1	0.00418
CFL	1	0.00418
Chambersburg	1	0.00418
Chamonix-Mont-Blanc	1	0.00418
Champaign Area	1	0.00418
Champaign Urbana	1	0.00418
Champaign-Urbana	1	0.00418
Champaign, IL	1	0.00418
Chantilly, VA	1	0.00418
Chapel hill	1	0.00418
Charles Town	1	0.00418
Charleston area	1	0.00418
Charlestown	1	0.00418
Charlotte area	1	0.00418
Charlotte metropolitan	1	0.00418
charlottesville	1	0.00418
Chaska	1	0.00418
Chatham	1	0.00418
Chatsworth	1	0.00418
Chehalis	1	0.00418
Chelsea	1	0.00418
Chemnitz	1	0.00418
Chennai	1	0.00418
Cheraw	1	0.00418
cherry hill	1	0.00418
Cherry hill	1	0.00418
Cherry Hill, NJ	1	0.00418
CHESAPEAKE	1	0.00418
Cheshire County	1	0.00418
Chester County	1	0.00418
Chester Springs, PA	1	0.00418
Chesterbrook	1	0.00418
Chesterfield, MI	1	0.00418
Chestertown	1	0.00418
Cheswick	1	0.00418
Cheyenne, Wy	1	0.00418
Chicacgo	1	0.00418
Chicago -remote position though	1	0.00418
Chicago (but distributed workplace)	1	0.00418
Chicago (remote)	1	0.00418
Chicago Area	1	0.00418
Chicago area mostly, but also the US and Canada	1	0.00418
Chicago but the company is remote	1	0.00418
Chicago metro	1	0.00418
Chicago Metro	1	0.00418
Chicago, IL (but I work remotely)	1	0.00418
Chicago/Deerfield (two offices)	1	0.00418
Chicagoland Area	1	0.00418
Chichester	1	0.00418
Chickasha	1	0.00418
Chillicothe	1	0.00418
Chilliwack, BC	1	0.00418
China Lake	1	0.00418
Chippenham	1	0.00418
Chirk	1	0.00418
choose not to answer	1	0.00418
Chula Vista	1	0.00418
Church Hill	1	0.00418
Cincinatti	1	0.00418
cincinnati	1	0.00418
Citrus Heights	1	0.00418
city in Middle Georgia	1	0.00418
City is too identifiable, state of NRW	1	0.00418
Ciudad de Panamá	1	0.00418
Clarendon Hills	1	0.00418
Clarksville	1	0.00418
Clawson	1	0.00418
Clayton	1	0.00418
Clearfield	1	0.00418
Clermont	1	0.00418
Cleveland Area	1	0.00418
Clifton	1	0.00418
Clifton Park	1	0.00418
Clinton Township	1	0.00418
Co Meath	1	0.00418
Cobourg	1	0.00418
Cocoa	1	0.00418
Coconut creek	1	0.00418
cody	1	0.00418
Coeur d’Alene	1	0.00418
Cold Spring, NY	1	0.00418
Collinsville	1	0.00418
Colombo	1	0.00418
Columbia Falls	1	0.00418
Columbia MD	1	0.00418
columbus	1	0.00418
Columbus, Boston	1	0.00418
Columbus, Indiana	1	0.00418
Colville	1	0.00418
Commack	1	0.00418
Company is based in Nashville, I work remotely from NYC	1	0.00418
Company is based in SF	1	0.00418
Company is in Glendale, California I am in Barrie, Ontario	1	0.00418
Company is in NYC, I'm remote in Boston	1	0.00418
Company is in San Antonio, I live 3 hours away in a rural community	1	0.00418
Concord home based	1	0.00418
Concord ma	1	0.00418
Concord, CA	1	0.00418
confidential	1	0.00418
Connecticut	1	0.00418
Conroe	1	0.00418
Conway	1	0.00418
Coon Rapids	1	0.00418
Corinth	1	0.00418
Corning	1	0.00418
Cornwall, Ontario	1	0.00418
corpus christi	1	0.00418
Corrigen	1	0.00418
Cortland	1	0.00418
Cortland, NY	1	0.00418
CORVALLIS	1	0.00418
Cotati	1	0.00418
Cottage Grove	1	0.00418
Country wide	1	0.00418
Coventry UK	1	0.00418
Covina	1	0.00418
Cranston, RI	1	0.00418
Creighton	1	0.00418
Crescent City	1	0.00418
Creston	1	0.00418
Crewe	1	0.00418
Crewe, Cheshire	1	0.00418
Crowley	1	0.00418
Crown Point	1	0.00418
Crownsville	1	0.00418
Crystal City	1	0.00418
Crystal Lake	1	0.00418
Cuernavaca	1	0.00418
Cuffley	1	0.00418
Cullowhee	1	0.00418
Cumbria	1	0.00418
Cupertino, CA	1	0.00418
currently remote	1	0.00418
Cuyahoga Falls	1	0.00418
Cypress	1	0.00418
Dachau	1	0.00418
Daegu	1	0.00418
Dagsboro	1	0.00418
Dahlgren	1	0.00418
Dahlonega	1	0.00418
Dallas / Fort Worth	1	0.00418
Dallas-Ft Worth	1	0.00418
Dallas, Texas	1	0.00418
Dallas, TX	1	0.00418
Dallas/Fort Worth	1	0.00418
Dallas/Fort Worth region	1	0.00418
Daly City	1	0.00418
Danbury CT	1	0.00418
Darmstadt	1	0.00418
Dartmouth	1	0.00418
Davenport	1	0.00418
Davenport area	1	0.00418
Daytona	1	0.00418
DC area	1	0.00418
DC Area	1	0.00418
DC Metro Area	1	0.00418
DC metro area, mostly DC and Montgomery County MD	1	0.00418
DC suburb	1	0.00418
DC Suburb	1	0.00418
Dearborn Heights	1	0.00418
Decherd	1	0.00418
Decline to mention	1	0.00418
Decline to provide	1	0.00418
Decline to State	1	0.00418
Declined	1	0.00418
Declined - Rural area	1	0.00418
Dedham	1	0.00418
Deerfield	1	0.00418
Defiance	1	0.00418
Deforest	1	0.00418
Deland	1	0.00418
DeLand	1	0.00418
Delaware County	1	0.00418
Delmar	1	0.00418
Delray beach	1	0.00418
Den bosch	1	0.00418
denver	1	0.00418
Denver - remote	1	0.00418
Denver - remote, company based in DC	1	0.00418
Denver (but remote - company is NYC)	1	0.00418
Denver Metro	1	0.00418
Denver-metro	1	0.00418
Denver, Chicago	1	0.00418
Derby, CT	1	0.00418
DeRidder	1	0.00418
Derry	1	0.00418
Des moines	1	0.00418
Detroit Area	1	0.00418
Detroit Metro area	1	0.00418
Detroit Metro Area	1	0.00418
devens	1	0.00418
Devens	1	0.00418
Dfw	1	0.00418
DFW area	1	0.00418
Dhaka	1	0.00418
Dickinson	1	0.00418
Didcot	1	0.00418
Dillard (I’m a telecommuter for an office based in Orlando FL)	1	0.00418
District of columbia	1	0.00418
Dobbs ferry	1	0.00418
Dodge City	1	0.00418
Doha	1	0.00418
Don’t want to answer	1	0.00418
Doncaster	1	0.00418
Dont want to say	1	0.00418
Dorset	1	0.00418
Dortmund	1	0.00418
Douglas	1	0.00418
Dresden	1	0.00418
Dripping Springs	1	0.00418
Drop down menur was not working. Indianapolis Indiana.	1	0.00418
Dubai	1	0.00418
Dubbo	1	0.00418
Dubli	1	0.00418
Dublin Ireland	1	0.00418
Dublin, CA	1	0.00418
Dublin, Ireland	1	0.00418
Duesseldorf	1	0.00418
Duncanville	1	0.00418
Dundee	1	0.00418
Dunfermline	1	0.00418
Dunnville	1	0.00418
Durham (remote employee in Vermont; salaries based on HQ in Durham)	1	0.00418
Durham area	1	0.00418
Durham, NC	1	0.00418
Duxford	1	0.00418
Eagan, MN	1	0.00418
Eagle	1	0.00418
East Bay	1	0.00418
East Brunswick	1	0.00418
East Chicago	1	0.00418
East Coast	1	0.00418
East Coast USA	1	0.00418
East Greenbush	1	0.00418
East Hampton	1	0.00418
East hartford	1	0.00418
EAST LANSING	1	0.00418
East Liverpool	1	0.00418
East Providence	1	0.00418
East Rockaway (Long Island)	1	0.00418
East Rutherford	1	0.00418
East Stroudsburg	1	0.00418
East Sussex	1	0.00418
East Syracuse	1	0.00418
Eastern	1	0.00418
Eastern MA	1	0.00418
Eastern MA (not Boston)	1	0.00418
Eastern WA	1	0.00418
Eastern WI City; approx. pop 50,000	1	0.00418
Easthampton	1	0.00418
Eatontown, NJ	1	0.00418
Eau Claire, WI	1	0.00418
eden prairie	1	0.00418
Eden prairie	1	0.00418
Eden Prairie, MN	1	0.00418
Edgartown ma	1	0.00418
Edgewood	1	0.00418
EDINA	1	0.00418
Edina - Minneapolis	1	0.00418
Edinburg	1	0.00418
Edinburgh (Scotland)	1	0.00418
Edinburgh, UK	1	0.00418
Edinburgh/London/Dublin/Manchester	1	0.00418
Edison, NJ	1	0.00418
Edmond	1	0.00418
EDMONTON	1	0.00418
Edson	1	0.00418
Egg Harbor Township	1	0.00418
Egg harbor twp	1	0.00418
Egham	1	0.00418
Eglin AFB	1	0.00418
Eindhoven	1	0.00418
El Dorado	1	0.00418
El jadida	1	0.00418
El Monte	1	0.00418
El segundo	1	0.00418
Elgin	1	0.00418
Elgin, Illinois	1	0.00418
Elkford	1	0.00418
Elko	1	0.00418
Elkton	1	0.00418
Elkton, VA	1	0.00418
Ellensburg, WA	1	0.00418
Elmhurst	1	0.00418
Elmwood Park	1	0.00418
Emporia	1	0.00418
Enfield	1	0.00418
Enon	1	0.00418
erie	1	0.00418
Erlanger	1	0.00418
Esbjerg	1	0.00418
Escanaba	1	0.00418
Espoo	1	0.00418
Essen	1	0.00418
Essex county	1	0.00418
Essex County	1	0.00418
Estero	1	0.00418
Estevan, Saskatchewan	1	0.00418
Ethel	1	0.00418
Eton, Berkshire	1	0.00418
Euclid	1	0.00418
Eugene, OR	1	0.00418
Everett, WA	1	0.00418
Ewing	1	0.00418
expat in Spanish speaking country	1	0.00418
Fairbury	1	0.00418
fairfax	1	0.00418
Fairfax (but now permanently virtual)	1	0.00418
Fairfax Station	1	0.00418
Fairfax VA	1	0.00418
Fairfax, va	1	0.00418
Fairfield County	1	0.00418
Falcon Heights	1	0.00418
Faribault	1	0.00418
Farmington hills	1	0.00418
federal way	1	0.00418
Fennimore	1	0.00418
Festus	1	0.00418
Few hours outside Columbus	1	0.00418
Field employee	1	0.00418
Field Employee	1	0.00418
Findlay	1	0.00418
Findlay, OH	1	0.00418
Fish Creek	1	0.00418
Fitchburg	1	0.00418
Fitchburg and Worcester	1	0.00418
Flint	1	0.00418
Florida	1	0.00418
Flowood	1	0.00418
Foothill Ranch, CA	1	0.00418
Fords	1	0.00418
Forest	1	0.00418
Forest Hill	1	0.00418
Forest Park	1	0.00418
Fort Belvoir	1	0.00418
Fort bragg	1	0.00418
Fort Campbell	1	0.00418
fort collins	1	0.00418
Fort Eustis	1	0.00418
Fort Fairfield	1	0.00418
Fort Hood	1	0.00418
Fort Knox	1	0.00418
Fort Lee	1	0.00418
Fort Meade	1	0.00418
Fort Pierce	1	0.00418
Fort Plain	1	0.00418
fort Polk	1	0.00418
Fort Rucker	1	0.00418
Fort Walton Beach	1	0.00418
Fort Walton Beach, FL	1	0.00418
Fortville	1	0.00418
foster city	1	0.00418
Fountain	1	0.00418
Fountain Valley	1	0.00418
Fox Valley	1	0.00418
Foxborough	1	0.00418
France	1	0.00418
Frankfurt a.M.	1	0.00418
Frankfurt am Main	1	0.00418
Frankfurt area	1	0.00418
Franklin Lakes	1	0.00418
Franklin Springs	1	0.00418
Franklin TN	1	0.00418
Franklin, MA	1	0.00418
Fraser	1	0.00418
Fraser valley	1	0.00418
Frederick, MD	1	0.00418
Free	1	0.00418
Freeland	1	0.00418
Freetown	1	0.00418
French Camp	1	0.00418
from home	1	0.00418
from home (company based in South of England near London)	1	0.00418
From home for a chicago based company	1	0.00418
Ft lauderdale	1	0.00418
Ft Myers	1	0.00418
Ft wayne	1	0.00418
Ft. Lauderdale	1	0.00418
Ft. Mitchell	1	0.00418
Ft. Walton	1	0.00418
Ft. Worth	1	0.00418
Full time remote employee (prior to pandemic)	1	0.00418
Full time remote employee, pre-pandemic	1	0.00418
Full time remote from my home near a major PA city	1	0.00418
Fully Remote - customers around the country	1	0.00418
Fully Remote (Greater Boston)	1	0.00418
Fully remote company based out of CA	1	0.00418
Fuquay Varina	1	0.00418
Fürth	1	0.00418
G	1	0.00418
Gadsden	1	0.00418
Gaitherburg	1	0.00418
Galax	1	0.00418
Galicia	1	0.00418
Gallatin	1	0.00418
Galloway/Atlantic City	1	0.00418
Garden city	1	0.00418
Garden Grove	1	0.00418
Gardiner	1	0.00418
Garland	1	0.00418
Garner	1	0.00418
Garnet Valley	1	0.00418
Gaston	1	0.00418
Gastonia	1	0.00418
Gateshead	1	0.00418
Gatesville	1	0.00418
Gatineau	1	0.00418
GC	1	0.00418
Geismar	1	0.00418
Geismar, LA	1	0.00418
Geneva	1	0.00418
Georgian Bay area	1	0.00418
Germantown	1	0.00418
Geyserville	1	0.00418
Ghent	1	0.00418
Gig Harbor	1	0.00418
Gilbertsville	1	0.00418
Glassboro	1	0.00418
Glen Burnie	1	0.00418
Glen Ellyn	1	0.00418
glendale	1	0.00418
Glendale, but I'm full time remote.	1	0.00418
Glendora	1	0.00418
Glennville	1	0.00418
Glenside	1	0.00418
Glenview	1	0.00418
Glenwood Springs	1	0.00418
Gloucestershire	1	0.00418
Gloucestershire (v. rural place of work)	1	0.00418
Gloversville, NY	1	0.00418
Goldsboro	1	0.00418
Goodyear	1	0.00418
Gorham/Portland	1	0.00418
Goring by Sea	1	0.00418
Goshen	1	0.00418
Göttingen	1	0.00418
Granbury	1	0.00418
Grand Haven	1	0.00418
Grand Prairie	1	0.00418
GRAND RAPIDS	1	0.00418
Grande Prairie Alberta Canada	1	0.00418
Grande Prairie, AB	1	0.00418
Grandville	1	0.00418
Grants	1	0.00418
Grants, NM	1	0.00418
Grays Harbor County	1	0.00418
Graz	1	0.00418
Great Falls	1	0.00418
Greater Atlanta Area	1	0.00418
Greater boston	1	0.00418
greater boston area	1	0.00418
Greater Chicago	1	0.00418
greater Chicagoland	1	0.00418
Greater Chicagoland	1	0.00418
Greater Madison Area	1	0.00418
Greater Minneapolis metro area	1	0.00418
Greater Nashville Area	1	0.00418
Greater New Orleans Metro Region	1	0.00418
Greater NYC area	1	0.00418
Greater NYC Area	1	0.00418
Greater NYC Metro	1	0.00418
Greater Philadelphia	1	0.00418
Greater Philadelphia Area	1	0.00418
Greater Portland	1	0.00418
Greater Sacramento	1	0.00418
greater Seattle area	1	0.00418
Greater Seattle area	1	0.00418
Greater Vancouver	1	0.00418
Greeley surrounding area	1	0.00418
Green bay	1	0.00418
Green Oaks	1	0.00418
Green River	1	0.00418
Greene	1	0.00418
Greeneville	1	0.00418
Greenfield, MA	1	0.00418
Greensburg	1	0.00418
Greenwood	1	0.00418
Greenwood Village	1	0.00418
Griffin	1	0.00418
Grimes	1	0.00418
Groningen	1	0.00418
Grove City (100% remote)	1	0.00418
Groveland	1	0.00418
Guelph, Ontario	1	0.00418
Guilford	1	0.00418
Gurley	1	0.00418
Gwinnett County	1	0.00418
Gympie	1	0.00418
Haddon field NJ	1	0.00418
Haddonfield	1	0.00418
Hadley	1	0.00418
hagerstown	1	0.00418
Haines city	1	0.00418
Halesowen	1	0.00418
Hamlet	1	0.00418
Hampshire	1	0.00418
hampton	1	0.00418
Hannibal	1	0.00418
Hannover	1	0.00418
Hanoi	1	0.00418
Hanover area	1	0.00418
Harlingen, TX (Remote Work)	1	0.00418
Harper Woods	1	0.00418
Harrisburg area	1	0.00418
Harrisburg Area	1	0.00418
Harrisburg Region	1	0.00418
Harrisburg, PA, USA	1	0.00418
Harrison	1	0.00418
Harrodsburg	1	0.00418
Hartford area	1	0.00418
Hartford County	1	0.00418
Hartforz	1	0.00418
Hartland	1	0.00418
Hartsville	1	0.00418
Hatboro	1	0.00418
Hauppauge	1	0.00418
Haverhill	1	0.00418
HAVERHILL	1	0.00418
Havre	1	0.00418
Hawalli	1	0.00418
hawera	1	0.00418
Hawley	1	0.00418
hazlet	1	0.00418
Healdsburg	1	0.00418
HELENA	1	0.00418
helsinki	1	0.00418
Hemel Hempstead	1	0.00418
Henrico	1	0.00418
Henrico County	1	0.00418
Henrico, VA	1	0.00418
Hercules	1	0.00418
Heredia	1	0.00418
Hereford	1	0.00418
Hermosillo, Sonora	1	0.00418
Herndon VA	1	0.00418
Herndon, Virginia	1	0.00418
Hersey	1	0.00418
Hershey	1	0.00418
Hertfordshire	1	0.00418
Hiawatha	1	0.00418
Hicksville	1	0.00418
Highland	1	0.00418
Highland Heights	1	0.00418
Highlands Ranch	1	0.00418
Hill City	1	0.00418
Hilliard	1	0.00418
Hillsdale	1	0.00418
Hilton Head area	1	0.00418
Hilversum	1	0.00418
Hinckley	1	0.00418
Hines	1	0.00418
Hingham	1	0.00418
Hobart	1	0.00418
HOBART	1	0.00418
Hobart, Tasmania	1	0.00418
Hoffman Estates	1	0.00418
Hoffman Estates (suburb of Chicago)	1	0.00418
Holbrook	1	0.00418
Holmdel	1	0.00418
Holmdel, NJ	1	0.00418
Home (near Montreal)	1	0.00418
Home based	1	0.00418
Home Based	1	0.00418
Home office	1	0.00418
Home plus travel to customers. Near Chicago	1	0.00418
Home worker, North East England	1	0.00418
Home/remote	1	0.00418
Hong Kong	1	0.00418
Honolilu	1	0.00418
Hood River	1	0.00418
Hopewell	1	0.00418
Hopkins	1	0.00418
Hopkinton	1	0.00418
Houghton	1	0.00418
Houston Area	1	0.00418
Houston but I am fully remote permanently	1	0.00418
Houston Texas	1	0.00418
Houston-Galveston-Brazoria area	1	0.00418
Houston, but the job is remote	1	0.00418
Howe	1	0.00418
Howell	1	0.00418
Hoyt Lakes	1	0.00418
HQ us in Cambridge, Ma but moving to the suburbs. I live right outside of Boston.	1	0.00418
Huber Heights	1	0.00418
Hudson Valley NY	1	0.00418
Hudson, NY	1	0.00418
Hughenden	1	0.00418
Hunt valley	1	0.00418
Huntersville	1	0.00418
Huntington (remote, HQ is in Charleston)	1	0.00418
Huntington beach	1	0.00418
Huntington Station	1	0.00418
Hutchinson	1	0.00418
Hüttigweiler	1	0.00418
Hyannis	1	0.00418
Hyattsville	1	0.00418
Hyde Park	1	0.00418
I don’t live in a city; I am VERY rural	1	0.00418
I left Boston for Indiana, so get a Boston salary in south bend	1	0.00418
I live in Orlando, but I am full-time remote	1	0.00418
I travel every week to different cities.	1	0.00418
I WFH full time for a remote organization (pre-pandemic)	1	0.00418
I work from home	1	0.00418
I work from home (permanent)	1	0.00418
I work majority for companies in the state of California.	1	0.00418
I work remotely	1	0.00418
I work remotely but in non-COVID times I work at client sites throughout the US	1	0.00418
I work remotely with clients across the country	1	0.00418
I work remotely, but based in San Diego	1	0.00418
I work remotely. My company is based out of Pittsburgh, PA	1	0.00418
I'm not comfortable answering that. Small town with something around 5k people.	1	0.00418
I'm permanently remote in MA, company is global.	1	0.00418
I'm sorry, I'd rather not say. But Alberta in Canada.	1	0.00418
I’m remote but my company is based in Massachusetts	1	0.00418
Idaho Falls	1	0.00418
Illinois, Iowa, Missouri	1	0.00418
Imperial	1	0.00418
In various cities. I work on projects through my employer for various financial organisations in the field of compliance & risk. My new project will be based in Amsterdam.	1	0.00418
Independence	1	0.00418
independence (cincinnati area)	1	0.00418
Indian Head	1	0.00418
Indian Head Park, IL	1	0.00418
Ingolstadt	1	0.00418
Innsbruck	1	0.00418
Invercargill	1	0.00418
Inverness	1	0.00418
Inwood	1	0.00418
Iowa	1	0.00418
Iowa City (Coralville)	1	0.00418
Iowa City / Cedar Rapids	1	0.00418
ipswich	1	0.00418
Iqaluit	1	0.00418
Ironwood	1	0.00418
Isabella	1	0.00418
Ishpeming	1	0.00418
Islamabad	1	0.00418
Islip	1	0.00418
Ithaca ny	1	0.00418
Ithaca NY	1	0.00418
Ithaca, NY	1	0.00418
Jackson Center	1	0.00418
Jackson Hole	1	0.00418
Jackson, MI	1	0.00418
Jackson, MS	1	0.00418
Jacksonville, FL	1	0.00418
Janesville	1	0.00418
Jasper	1	0.00418
Jax	1	0.00418
Jena	1	0.00418
Jenkintown	1	0.00418
Jersey city	1	0.00418
JERSEY CITY	1	0.00418
Jersey City, NJ	1	0.00418
Jerusalem	1	0.00418
Jessup	1	0.00418
Job is based in DC, but I go to where disasters are	1	0.00418
Johannesburg	1	0.00418
Johnston, RI	1	0.00418
Johnstown	1	0.00418
Joint Base Andrews, MD	1	0.00418
Joppatowne (Near Baltimore, MD)	1	0.00418
Jurupa Valley	1	0.00418
Kabul	1	0.00418
Kajaani	1	0.00418
Kamloops	1	0.00418
Kampala	1	0.00418
Kannapolis	1	0.00418
Kansa City	1	0.00418
Kansas City Region	1	0.00418
Kansas City, MO	1	0.00418
Kapaa	1	0.00418
Karlsruhe	1	0.00418
Kassel	1	0.00418
Keiser	1	0.00418
Kelown	1	0.00418
Kenora	1	0.00418
Kentwood	1	0.00418
Ketchikan	1	0.00418
Kettering	1	0.00418
Key West	1	0.00418
Keyport	1	0.00418
Keyser	1	0.00418
Kiel	1	0.00418
Kigali	1	0.00418
Kilkenny	1	0.00418
Kingsburg	1	0.00418
Kingston Ontario	1	0.00418
Kingston, Ontario	1	0.00418
Kipling	1	0.00418
Kitchener Waterloo	1	0.00418
Kitchener-Waterloo	1	0.00418
Kitchener, ontario	1	0.00418
Kitimat, BC	1	0.00418
Kiyv	1	0.00418
Klamath Falls	1	0.00418
Knightdale	1	0.00418
Köln	1	0.00418
Kragujevac	1	0.00418
Krakow	1	0.00418
Krum	1	0.00418
Kuala Lumpur	1	0.00418
La Laguna	1	0.00418
La Mirada	1	0.00418
La Porte	1	0.00418
Lacey	1	0.00418
Lacey Township	1	0.00418
Lacombe	1	0.00418
Laconia	1	0.00418
Lagos	1	0.00418
Laguna Hills	1	0.00418
Laguna niguel	1	0.00418
LagunaHills	1	0.00418
Lahaina	1	0.00418
Laie	1	0.00418
Lake Buena Vista	1	0.00418
Lake Charles	1	0.00418
Lake City	1	0.00418
Lake Elsinor	1	0.00418
Lake Forest Park	1	0.00418
Lake Mills	1	0.00418
Lake Oswego	1	0.00418
Lake Tahoe	1	0.00418
Lakeville	1	0.00418
Lakewood (live in Denver)	1	0.00418
Lakewood Ranch	1	0.00418
Lakewood, CO	1	0.00418
Lambertville	1	0.00418
Lamham	1	0.00418
Lancashire	1	0.00418
Lancaster PA	1	0.00418
Lancaster UK	1	0.00418
Lancaster, Pa	1	0.00418
Langley	1	0.00418
Langley, BC	1	0.00418
Lanham	1	0.00418
Lanham, MD	1	0.00418
Lansdowne	1	0.00418
Laplace	1	0.00418
Laredo	1	0.00418
Large metro area	1	0.00418
Large town/small city	1	0.00418
Las begas	1	0.00418
Las Palmas	1	0.00418
las vegas	1	0.00418
Las vegas	1	0.00418
Las Vegas (Techinically remote)	1	0.00418
Latham	1	0.00418
Lathma	1	0.00418
Lawrenceburg	1	0.00418
Lawton	1	0.00418
Le Center	1	0.00418
Leadville	1	0.00418
Leavenworth	1	0.00418
Leeds,UK	1	0.00418
Leesburg VA	1	0.00418
LeHigh Valley	1	0.00418
Lehighton	1	0.00418
Leigh, Greater Manchester	1	0.00418
Lemgo	1	0.00418
Lenoir	1	0.00418
Lenox	1	0.00418
Leonia	1	0.00418
lethbridge	1	0.00418
Lethbridge	1	0.00418
Lewisburg	1	0.00418
lewisville	1	0.00418
LEXINGTON	1	0.00418
Lexington Park	1	0.00418
Libby	1	0.00418
Liberal	1	0.00418
Liberty	1	0.00418
Libertyville	1	0.00418
Lillington	1	0.00418
Limerick	1	0.00418
Limestone	1	0.00418
Lincoln, NE	1	0.00418
Lincoln, RI	1	0.00418
Lincolnshire	1	0.00418
Linden	1	0.00418
Lindon	1	0.00418
Linthicum, MD	1	0.00418
Little Chute	1	0.00418
Little rock	1	0.00418
Littlehampton	1	0.00418
Littleton	1	0.00418
Littleton, MA / Manchester, NH	1	0.00418
Ljubljana	1	0.00418
Lloydminster	1	0.00418
Lockport	1	0.00418
Lodi	1	0.00418
Lombard	1	0.00418
Lomdon	1	0.00418
Lompoc	1	0.00418
london	1	0.00418
LONDON	1	0.00418
London (but remote)	1	0.00418
London (outskirts - Croydon)	1	0.00418
London / remote	1	0.00418
London / Remote	1	0.00418
London suburb	1	0.00418
London, ON	1	0.00418
London, ON CAN	1	0.00418
London, Ontario, Canada	1	0.00418
London/home	1	0.00418
Long Beach, Ca	1	0.00418
Long Island, NY	1	0.00418
longbeach	1	0.00418
longford	1	0.00418
Longmont (remote)	1	0.00418
Lons Le Saunier	1	0.00418
Lopen	1	0.00418
Lorton	1	0.00418
Los A6	1	0.00418
los alamos	1	0.00418
Los alamos	1	0.00418
Los Altos	1	0.00418
Los Altos Hills	1	0.00418
LOS ANGELES	1	0.00418
Los Angeles (but my job is remote)	1	0.00418
Los Angeles area	1	0.00418
Los Angeles Metro	1	0.00418
Los Angeles Metro Area	1	0.00418
Los Angeles, but I work with people in Europe including the company owner	1	0.00418
Los Angeles, or I travel for work	1	0.00418
Los Angles	1	0.00418
Los Gatos	1	0.00418
Loudoun County	1	0.00418
LOUISVILLE	1	0.00418
Louisville (remote)	1	0.00418
lowell	1	0.00418
Lower Hutt, Wellington	1	0.00418
Lusby	1	0.00418
Lutherville	1	0.00418
Luxemburg	1	0.00418
Lyndhurst	1	0.00418
Lyndon	1	0.00418
Lynnfield (Boston metro area)	1	0.00418
lynnwood	1	0.00418
Lyons	1	0.00418
Maassluis	1	0.00418
Maastricht	1	0.00418
Machesney Park	1	0.00418
Macomb	1	0.00418
Macon, GA	1	0.00418
Madera	1	0.00418
madison	1	0.00418
Madison Heights	1	0.00418
Madison WI	1	0.00418
Madison, WI	1	0.00418
Madison, Wisconsin	1	0.00418
Madisonville	1	0.00418
Madrid, Spain	1	0.00418
Magna	1	0.00418
Maidenhead	1	0.00418
Maidstone	1	0.00418
Maine	1	0.00418
Mainz	1	0.00418
Maitland	1	0.00418
Major metropolitan area	1	0.00418
Major state metro area	1	0.00418
Málaga	1	0.00418
Malaysia	1	0.00418
Malvern, PA	1	0.00418
Manasquan, NJ	1	0.00418
Manchester, England	1	0.00418
Manchesyer	1	0.00418
Mandan	1	0.00418
Manhasset	1	0.00418
Manhasset (Long Island)	1	0.00418
Manitowoc	1	0.00418
Mansfield	1	0.00418
Maple grove	1	0.00418
Maple Shade	1	0.00418
Maquoketa	1	0.00418
Maricopa County	1	0.00418
MARIETTA	1	0.00418
Marina	1	0.00418
Marissa	1	0.00418
Marlborough MA	1	0.00418
Marlton, NJ	1	0.00418
Marseille	1	0.00418
Marshall	1	0.00418
Marshalltown	1	0.00418
marshfield	1	0.00418
Martinez	1	0.00418
Martinez (Bay Area)	1	0.00418
Mascoutah	1	0.00418
Mason city	1	0.00418
massachusetts	1	0.00418
Massillon	1	0.00418
Mattawan	1	0.00418
Mauldin	1	0.00418
Maumee	1	0.00418
Mayagüez	1	0.00418
McAllen	1	0.00418
McClean	1	0.00418
Mccomb	1	0.00418
McKinney	1	0.00418
MCKINNEY	1	0.00418
McLean though I travel some	1	0.00418
Mclean, VA outside of DC	1	0.00418
MCOL New England	1	0.00418
Mead	1	0.00418
Meadville	1	0.00418
Mebourne	1	0.00418
Mechelen	1	0.00418
Medford Oregon	1	0.00418
Medical Lake	1	0.00418
Medium size city	1	0.00418
Melbourne Australia	1	0.00418
Melbourne, Victoria	1	0.00418
Mellbourne	1	0.00418
Memphis (but work 100% remote)	1	0.00418
Menasha	1	0.00418
Mendota Heights	1	0.00418
Menomonie	1	0.00418
Mentor, Ohio	1	0.00418
Meriden	1	0.00418
Merrick - my home, but we do have an office in NYC. Main office is in Seattle, WA.	1	0.00418
Merrit Island	1	0.00418
Merseyside	1	0.00418
Mesa, Arizona	1	0.00418
Methuen	1	0.00418
metro	1	0.00418
Metro Boston	1	0.00418
Metro Detroit area	1	0.00418
Metro NYC	1	0.00418
Metro west	1	0.00418
Metro-Detroit	1	0.00418
Metro-west	1	0.00418
Metrowest area	1	0.00418
Metrowest Boston	1	0.00418
Metrowest Boston area	1	0.00418
Mettawa	1	0.00418
Miami area	1	0.00418
Miami FTL metro area	1	0.00418
Michigan	1	0.00418
Mid-Michigan	1	0.00418
Mid-size city	1	0.00418
middleburg	1	0.00418
Middleburg	1	0.00418
Middleburg Heights	1	0.00418
Middlesbrough	1	0.00418
Midlands	1	0.00418
Midlothian	1	0.00418
Midsized city	1	0.00418
Midwest	1	0.00418
Midwest IL	1	0.00418
Midwest region	1	0.00418
Mill Valley	1	0.00418
Milledgeville	1	0.00418
Millville	1	0.00418
Milton Keynes, UK	1	0.00418
Milwaukee metro	1	0.00418
Milwaukee metro area	1	0.00418
Minneapilis	1	0.00418
MINNEAPOLIS	1	0.00418
Minneapolis metro	1	0.00418
Minneapolis, MN	1	0.00418
Minneapolis/St Paul metro area	1	0.00418
Minneapollis	1	0.00418
Minnepolis	1	0.00418
Minnesota	1	0.00418
Minnespolis	1	0.00418
Minocqua	1	0.00418
Minot	1	0.00418
Miseneheimer	1	0.00418
Mission Viejo	1	0.00418
MISSOULA	1	0.00418
missouri city	1	0.00418
Mitchell	1	0.00418
Mohegan Lake	1	0.00418
Moncks Corner	1	0.00418
Monmouth	1	0.00418
Mono, Ontario	1	0.00418
Monongahela	1	0.00418
Monrovia	1	0.00418
Montana	1	0.00418
Montclair	1	0.00418
Montclair (not actual city, but same region for anonymity)	1	0.00418
Montevideo	1	0.00418
Montgomery, AL	1	0.00418
Montgomeryville	1	0.00418
Monticello	1	0.00418
Montpellier	1	0.00418
montreal	1	0.00418
Montrose	1	0.00418
Moon Twp. PA	1	0.00418
Mooresville	1	0.00418
Moose Jaw	1	0.00418
Moose Jaw, sk.	1	0.00418
Morenci	1	0.00418
Morris Plains	1	0.00418
Morristown (REMOTE)	1	0.00418
Moshiem	1	0.00418
Mount Gambier	1	0.00418
Mount laurel	1	0.00418
Mount Pleasant	1	0.00418
Mount Prospect	1	0.00418
Mount Washington	1	0.00418
Mountain view	1	0.00418
Mountainside	1	0.00418
Mountainview	1	0.00418
Mountlake Terrace	1	0.00418
mt arlington	1	0.00418
Mt Pleasant	1	0.00418
Mt Prospect	1	0.00418
Mt. Washington	1	0.00418
Multiple	1	0.00418
Mumbai	1	0.00418
Münster	1	0.00418
Murrieta	1	0.00418
Muscatine	1	0.00418
Muskego	1	0.00418
Muskegon	1	0.00418
My city + industry would ID my employer	1	0.00418
My house	1	0.00418
My job is remote for everyone at my company, but I live in Brooklyn	1	0.00418
Mystic	1	0.00418
N.A.	1	0.00418
N/A - County	1	0.00418
N/A - remote work	1	0.00418
n/a - work from home	1	0.00418
N/A cloud-based	1	0.00418
N/A-Remote	1	0.00418
N\A	1	0.00418
NA (remote)	1	0.00418
NA (remote). Live near Boston, work is based in upstate NY	1	0.00418
NA (work from home in small town, decline to specify)	1	0.00418
Naas	1	0.00418
Nampa	1	0.00418
Nantes	1	0.00418
Nanticoke	1	0.00418
Naperville (Chicago area)	1	0.00418
Naperville IL	1	0.00418
NAPLES	1	0.00418
Naples, Fl	1	0.00418
Narnia	1	0.00418
Narragansett	1	0.00418
Nashville (at home, remote)	1	0.00418
Nashville metro area	1	0.00418
Nashville Metro Area	1	0.00418
National-Remote	1	0.00418
Naval Base	1	0.00418
NE Ohio	1	0.00418
Near Albany	1	0.00418
near Ann Arbor	1	0.00418
Near Boston	1	0.00418
near Buffalo, NY	1	0.00418
Near Chicago	1	0.00418
Near Indianapolis	1	0.00418
Near Manchester, UK	1	0.00418
Near Sacramento (remote work)	1	0.00418
Near Salt Lake City	1	0.00418
near San Antonio	1	0.00418
Near Seattle	1	0.00418
Nelson	1	0.00418
Neopit	1	0.00418
Neptune Beach	1	0.00418
New albany	1	0.00418
New Berlin	1	0.00418
New Bern	1	0.00418
New Braunfels	1	0.00418
New Brighton	1	0.00418
New Bruswick	1	0.00418
New City, NY	1	0.00418
New Glarus	1	0.00418
New Haven metropolitan area	1	0.00418
New Haven/Remote	1	0.00418
New Hyde park	1	0.00418
New Kensington	1	0.00418
New Lenox	1	0.00418
new Orleans	1	0.00418
New orleans	1	0.00418
New Tecumseth	1	0.00418
New Westminster	1	0.00418
New Yor	1	0.00418
New YOrk	1	0.00418
New York Buty	1	0.00418
new York City	1	0.00418
New York CIty	1	0.00418
New York City (The Bronx)	1	0.00418
New York City Suburbs	1	0.00418
New York City, currently remote	1	0.00418
New york City/Manhattan	1	0.00418
New York, New York	1	0.00418
New YorknCity	1	0.00418
Newark (remote now)	1	0.00418
Newark, NJ	1	0.00418
Newberg	1	0.00418
newcastle	1	0.00418
Newcastle, NSW, Australia	1	0.00418
Newcastle, UK	1	0.00418
Newport beach	1	0.00418
Newport Beach, CA	1	0.00418
Newport News, VA	1	0.00418
Newton Falls	1	0.00418
Newton Poppleford	1	0.00418
Niagara Falls	1	0.00418
Niagara Region	1	0.00418
Nijmegen	1	0.00418
no	1	0.00418
no answer	1	0.00418
No city	1	0.00418
Noble	1	0.00418
Noblesville	1	0.00418
none	1	0.00418
None, work from home	1	0.00418
Noordwijk	1	0.00418
Nope	1	0.00418
Norco	1	0.00418
Normal I’ll	1	0.00418
Norman,OK	1	0.00418
Norristowb	1	0.00418
Norristown, PA	1	0.00418
North	1	0.00418
North Adams	1	0.00418
north bay	1	0.00418
North Bergen, NJ	1	0.00418
North Brunswick	1	0.00418
North Cape, PE	1	0.00418
North Charleston	1	0.00418
North Clarendon	1	0.00418
North Coast	1	0.00418
North Fort Myers	1	0.00418
North Georgia	1	0.00418
North Hempstead	1	0.00418
North Jersey	1	0.00418
North Kingstown	1	0.00418
North Mankato	1	0.00418
North Miami	1	0.00418
North of Portland	1	0.00418
North of Toronto	1	0.00418
North Plainfield	1	0.00418
North Platte	1	0.00418
North Reading	1	0.00418
North Sydney	1	0.00418
North Wales	1	0.00418
North West	1	0.00418
Northampton Ma	1	0.00418
Northborough	1	0.00418
Northbrook/ Charlotte	1	0.00418
Northcentral WI	1	0.00418
Northeast Florida	1	0.00418
Northeast Ohio suburb	1	0.00418
Northern B.C.	1	0.00418
Northern California	1	0.00418
northern CO	1	0.00418
Northern Front Range	1	0.00418
Northern Michigan	1	0.00418
northern new york	1	0.00418
northern vermont	1	0.00418
Northern Virginia	1	0.00418
Northern Virginia (Washington, DC metro)	1	0.00418
Northlake	1	0.00418
Northumberland	1	0.00418
Northville	1	0.00418
Northwest	1	0.00418
Northwest Chicago suburbs	1	0.00418
Northwest Georgia	1	0.00418
Northwest Lower MI	1	0.00418
Norton Shores	1	0.00418
Norwood MA	1	0.00418
Not a city	1	0.00418
Not answering	1	0.00418
Not applicable	1	0.00418
Not Applicable (Remote work)	1	0.00418
Not Denver	1	0.00418
Not Detroit	1	0.00418
Not disclosed	1	0.00418
Not Disclosing	1	0.00418
Not identifying, but the Central Valley, CA	1	0.00418
Not provided	1	0.00418
Not saying	1	0.00418
Nottingham, UK	1	0.00418
Nottoway County	1	0.00418
Novato, CA	1	0.00418
nr. London	1	0.00418
NRW	1	0.00418
Nueremberg	1	0.00418
NULL	1	0.00418
NY Metro	1	0.00418
NY metro area but not NYC	1	0.00418
NY Suburb	1	0.00418
Nyack	1	0.00418
nyc	1	0.00418
NYC - Queens	1	0.00418
NYC (but I work remote)	1	0.00418
NYC (remotely)	1	0.00418
NYC metro area	1	0.00418
NYC, currently WFH	1	0.00418
NYC, NY	1	0.00418
Oak Lawn	1	0.00418
Oak Park	1	0.00418
Oakbrook	1	0.00418
Oakbrook Terrace	1	0.00418
oakland	1	0.00418
Oakland and Hayward	1	0.00418
Oakland CA	1	0.00418
Oakland, CA	1	0.00418
Oaxaca de Juarez (home). Company I work for is based in Los Ángeles	1	0.00418
Ocala	1	0.00418
Ocean	1	0.00418
Ocean County	1	0.00418
Office was in Chicago, now WFH in WI	1	0.00418
Office: Lansing, MI and wfh Ypsilani MI	1	0.00418
Ogden - but I work remote. Company is based in MD	1	0.00418
Okanogan	1	0.00418
OKC	1	0.00418
Okemos	1	0.00418
Olean	1	0.00418
Olympia, WA	1	0.00418
Olympic Peninsula	1	0.00418
Omaha Metro	1	0.00418
Onaha	1	0.00418
One of the largest in the state	1	0.00418
Onley	1	0.00418
Orang	1	0.00418
Orange county	1	0.00418
Orange park	1	0.00418
Orange Park	1	0.00418
Orange, MA	1	0.00418
Orangevale	1	0.00418
Oregon	1	0.00418
Oregon City	1	0.00418
Orillia	1	0.00418
Orkney	1	0.00418
Orlando metro area	1	0.00418
Orlando, I work in United States at a Consulate for Mexican Government	1	0.00418
Orléans	1	0.00418
Ormond Beach	1	0.00418
Orono	1	0.00418
Osceola	1	0.00418
Oshawa	1	0.00418
Oswego	1	0.00418
Ottawa area	1	0.00418
Ottawa ON Canada	1	0.00418
Ottawa-Gatineau	1	0.00418
Ottawa, ON	1	0.00418
Ottawa/Gatineau	1	0.00418
Ottoville, OH	1	0.00418
Outer Island	1	0.00418
Outer London	1	0.00418
Outside Boston	1	0.00418
Outside London	1	0.00418
outside of Boston	1	0.00418
outside philadelphia	1	0.00418
OUtside Philadelphia	1	0.00418
Outside the Twin Cities	1	0.00418
Owen Sound	1	0.00418
Oxford, UK	1	0.00418
Oxnard	1	0.00418
Pacifica	1	0.00418
Paducah	1	0.00418
Palermo	1	0.00418
Palm Beach	1	0.00418
Palm Beach, Fl	1	0.00418
Palm Coast	1	0.00418
Palmyra	1	0.00418
Palo Alto, CA	1	0.00418
Panama City Beach	1	0.00418
Panhandle of FL	1	0.00418
Panhandle region	1	0.00418
Paragould	1	0.00418
Parid	1	0.00418
Park City	1	0.00418
Park City (remote/WFH)	1	0.00418
Parker	1	0.00418
Parkersburg	1	0.00418
Parma	1	0.00418
Parry Sound	1	0.00418
Pascagoula, MS	1	0.00418
Pasco	1	0.00418
Passaic	1	0.00418
Passaic County	1	0.00418
Passau	1	0.00418
Patchogue	1	0.00418
Paulding	1	0.00418
Pawtucket	1	0.00418
Payroll is out of Cincinnati	1	0.00418
Peabody	1	0.00418
Peabody, MA	1	0.00418
Peace River	1	0.00418
Pearisburg	1	0.00418
Pearl City	1	0.00418
Pendleton	1	0.00418
pensacola	1	0.00418
Penticton	1	0.00418
Peoria, IL	1	0.00418
Permanent Remote to Toronto	1	0.00418
Perth, Western Australia	1	0.00418
Peterlee	1	0.00418
Petoskey	1	0.00418
Phialdelphia	1	0.00418
Philadelphia (suburbs)	1	0.00418
Philadelphia and Pittsburgh	1	0.00418
Philadelphia area	1	0.00418
Philadelphia Area	1	0.00418
Philadelphia Suburbs	1	0.00418
Philadelphia, PA	1	0.00418
Philadelphia, PA Suburbs	1	0.00418
Philadelphiai	1	0.00418
Philadephia	1	0.00418
Philadlephia	1	0.00418
Philiadelpia	1	0.00418
philly	1	0.00418
Philly	1	0.00418
Phnom Penh	1	0.00418
Phoenix Area	1	0.00418
Phoenix, AZ	1	0.00418
Phoenix/Tempe	1	0.00418
Phoenixville	1	0.00418
Pinehurst	1	0.00418
Pinole	1	0.00418
Pipersville	1	0.00418
Piscataway, NJ	1	0.00418
Pittaburgh	1	0.00418
PITTSBURGH	1	0.00418
Pittsburgh area	1	0.00418
Pittsfiel	1	0.00418
Plainfield	1	0.00418
Plainville	1	0.00418
Plant City	1	0.00418
Platte City	1	0.00418
Plattsburgh, NY	1	0.00418
Plattsville	1	0.00418
Pleasant grove	1	0.00418
Pleasant Grove	1	0.00418
Pleasant Hill	1	0.00418
Pleasant Prarie	1	0.00418
Plymouth (UK)	1	0.00418
Plymouth England	1	0.00418
Pollock Pines	1	0.00418
Pomfret	1	0.00418
Pompano Beach	1	0.00418
PONTYPOOL	1	0.00418
Poole	1	0.00418
Port Austin	1	0.00418
Port Carling, Muskoka, Ontario	1	0.00418
Port Chester	1	0.00418
Port Coquitlam	1	0.00418
Port washington	1	0.00418
Port Washington	1	0.00418
Portage la Prairie, Manitoba	1	0.00418
Porter County	1	0.00418
PORTLAND	1	0.00418
Portland (I work remote for a company out of state)	1	0.00418
Portland metro	1	0.00418
Portland metro area	1	0.00418
Portland Metro area	1	0.00418
Portland OR	1	0.00418
Portland, ME area	1	0.00418
Portland, or	1	0.00418
Portland, OR (remote for a company in CA)	1	0.00418
Portlanf	1	0.00418
Portree	1	0.00418
Posen	1	0.00418
Poth	1	0.00418
Potland	1	0.00418
Poulsbo	1	0.00418
Powhatan	1	0.00418
Prefer not to answer, moderate size canadian city	1	0.00418
prefer not to identify	1	0.00418
Prefer not to provide	1	0.00418
Prefer not to say (Montana is small!)	1	0.00418
prefer not to say, identifiable information when combined with my job title	1	0.00418
Preston, Lancashire	1	0.00418
Princeton area	1	0.00418
Prineville	1	0.00418
Prospect Park	1	0.00418
providence	1	0.00418
Puget Sound Region	1	0.00418
Pulaski	1	0.00418
Puyallup, WA	1	0.00418
QAC	1	0.00418
Quebec	1	0.00418
Québec	1	0.00418
Quebec city	1	0.00418
Queen Creek	1	0.00418
Queens Village	1	0.00418
Queens/Nassau	1	0.00418
Queensbury	1	0.00418
Quito	1	0.00418
Ra'anana	1	0.00418
RACINE	1	0.00418
Radnor	1	0.00418
Raeford	1	0.00418
raleigh	1	0.00418
Raleigh (remote out of VA)	1	0.00418
Raleigh Durham	1	0.00418
Raleigh NC	1	0.00418
Raleigh; role based out of Boston MA	1	0.00418
Ramsbottom	1	0.00418
Rancho Cucamonga	1	0.00418
Rancho Santa Margarita	1	0.00418
rapid city	1	0.00418
Raritan	1	0.00418
Rather not answer	1	0.00418
rather not say	1	0.00418
Rather not say	1	0.00418
rather not say (too identifiable)	1	0.00418
Raynham	1	0.00418
Red bank	1	0.00418
Red Deer	1	0.00418
Red Hook	1	0.00418
Redacted	1	0.00418
redacted (sorry)	1	0.00418
Redding CA	1	0.00418
redmond	1	0.00418
Redwood City, CA	1	0.00418
Refuse to Answer	1	0.00418
Regensburg	1	0.00418
Regina (SK)	1	0.00418
Regina, SK	1	0.00418
regional	1	0.00418
regional city	1	0.00418
Reidsville	1	0.00418
Remote -	1	0.00418
Remote - HQ is in DC	1	0.00418
Remote - Lansdale	1	0.00418
Remote - previously NYC	1	0.00418
Remote - Washington DC Area	1	0.00418
Remote (Chicago)	1	0.00418
Remote (live in Nottingham)	1	0.00418
Remote (Olympia)	1	0.00418
Remote (org based out of DC metro area)	1	0.00418
Remote (Southern CA)	1	0.00418
Remote (this shouldn't be required)	1	0.00418
Remote Based	1	0.00418
Remote but NYC	1	0.00418
Remote job	1	0.00418
Remote many cities	1	0.00418
Remote near Worcester	1	0.00418
Remote Office	1	0.00418
Remote position	1	0.00418
Remote US	1	0.00418
Remote WFH currently	1	0.00418
Remote work	1	0.00418
Remote work from home	1	0.00418
Remote work in CA, company is in WA	1	0.00418
Remote work; Ontario-wide	1	0.00418
Remote Worker	1	0.00418
Remote Worker out of Wilmington	1	0.00418
Remote worker, staffed out of Cincinnati	1	0.00418
Remote--don't work in same state as my employer is located	1	0.00418
Remote, based out of Baltimore, MD metro area	1	0.00418
Remote, company is based in Raleigh NC	1	0.00418
Remote, from a small city in Romania	1	0.00418
Remote, North Carolina	1	0.00418
Remote; based out of Hudson	1	0.00418
Remote. Company is in Boston, I am in SF	1	0.00418
Remote/ home	1	0.00418
Remote/London	1	0.00418
Remote/NYC HQ	1	0.00418
Remotely - Stamford HQ	1	0.00418
Remotely in NC for DC employer	1	0.00418
remove	1	0.00418
reno	1	0.00418
Reno/Sparks	1	0.00418
renton	1	0.00418
Research Triangle region	1	0.00418
Reston, VA	1	0.00418
Rexburg	1	0.00418
Reynoldsburg	1	0.00418
Richfield	1	0.00418
Richland, WA	1	0.00418
RICHMOND	1	0.00418
RICHMOND VA	1	0.00418
Richmond VIC	1	0.00418
Ridgecrest	1	0.00418
Ridgefield Park	1	0.00418
Ringwood	1	0.00418
Rio de Janeiro	1	0.00418
Rio Rancho	1	0.00418
River Grove	1	0.00418
Riverside and San Bernardino	1	0.00418
Riverside, CA	1	0.00418
ROCHESTER	1	0.00418
Rochester (remote-company based in SF, CA)	1	0.00418
Rock Island	1	0.00418
Rock Springs	1	0.00418
Rocklin (Sacramento area)	1	0.00418
Rockport	1	0.00418
Rocky Hill	1	0.00418
Rohnert Park, CA	1	0.00418
Romeoville	1	0.00418
Rootstown	1	0.00418
Roseland	1	0.00418
Roselle	1	0.00418
Round Rock	1	0.00418
Roxboro	1	0.00418
Royersford	1	0.00418
ROYERSFORD	1	0.00418
RTP	1	0.00418
RTP (Raleigh-Durham)	1	0.00418
Rugby	1	0.00418
Rupert	1	0.00418
Rural - northern	1	0.00418
Rural Alberta	1	0.00418
rural area	1	0.00418
Rural Area	1	0.00418
Rural California	1	0.00418
rural college community	1	0.00418
rural college town	1	0.00418
rural Florida	1	0.00418
Rural Florida Panhandle	1	0.00418
Rural Iowa	1	0.00418
Rural Ireland	1	0.00418
Rural Missouri	1	0.00418
Rural New England	1	0.00418
Rural NW IL	1	0.00418
Rural Richmond Area	1	0.00418
Rural school division	1	0.00418
rural small town	1	0.00418
Rural small town western NY	1	0.00418
Rural Southwest England	1	0.00418
Rural town (prefer not to disclose)	1	0.00418
Rural Upper Peninsula	1	0.00418
Rural west-central MN	1	0.00418
Rural/Suburban	1	0.00418
Ruther Glen	1	0.00418
Rutland	1	0.00418
Rye Brook	1	0.00418
Saarbrücken	1	0.00418
Sabetha	1	0.00418
sacramento	1	0.00418
Sacramento CA	1	0.00418
Saddle Brook	1	0.00418
Saint Charles	1	0.00418
Saint Helena	1	0.00418
Saint Paul Park	1	0.00418
Saint Paul, MN	1	0.00418
Salamanca	1	0.00418
Salem (remote for Boston Mass)	1	0.00418
Salem, MA	1	0.00418
Salisbury	1	0.00418
Salmon	1	0.00418
Salmon Arm	1	0.00418
Salt lake	1	0.00418
Sam Francisco	1	0.00418
Sam Mateo	1	0.00418
San Angelo, Texas	1	0.00418
San antoni	1	0.00418
San Bruno	1	0.00418
San Carlos	1	0.00418
San Clemente	1	0.00418
San Deigo	1	0.00418
San Dieg	1	0.00418
san diego	1	0.00418
San Diego, CA	1	0.00418
San Francisco (nominally; I work from home now due to COVID)	1	0.00418
San Francisco, but company is Boston based	1	0.00418
San Francisco, CA	1	0.00418
San Fransico	1	0.00418
San Fransisco	1	0.00418
san jose	1	0.00418
San jose	1	0.00418
San Jose, CA and Tulsa, OK	1	0.00418
San juan	1	0.00418
San luis obispo	1	0.00418
San Marino	1	0.00418
San Mateo, CA	1	0.00418
san rafael	1	0.00418
Sandy Springs	1	0.00418
Santa Ana, CA	1	0.00418
Santa barbara	1	0.00418
Santa Barbara, CA	1	0.00418
Santa Clarita	1	0.00418
Santee	1	0.00418
Santiago	1	0.00418
Saranac Lake (it's a small town!)	1	0.00418
Sarnia	1	0.00418
saskatoon	1	0.00418
Sauk Centre	1	0.00418
Sauk City	1	0.00418
Sault Ste Marie	1	0.00418
Sault Ste. Marie	1	0.00418
Sausalito	1	0.00418
Saxonburg	1	0.00418
SC	1	0.00418
Schaumburg (Chicago Metro Area)	1	0.00418
Schenectady, NY	1	0.00418
Schiphol Rijk	1	0.00418
Scituate	1	0.00418
Scotch Plains	1	0.00418
Scotland	1	0.00418
Scotland, Ont	1	0.00418
Scott Depot	1	0.00418
Scotts Valley	1	0.00418
Scottsbluff	1	0.00418
Scranton/Wilkes-Barre	1	0.00418
Scunthorpe	1	0.00418
SeaTac	1	0.00418
Seatle, WA	1	0.00418
Seatlle	1	0.00418
Seattle (but fully remote)	1	0.00418
Seattle (company is in Redmond)	1	0.00418
Seattle metro	1	0.00418
Seattle Metro	1	0.00418
Seattle, wa	1	0.00418
Seattle, Wa	1	0.00418
Seattle, Washington	1	0.00418
Sebastopol	1	0.00418
Sebring	1	0.00418
Secaucus	1	0.00418
Secaucus NJ	1	0.00418
See above	1	0.00418
Seekonk	1	0.00418
semirural city	1	0.00418
Seneca	1	0.00418
Sequim	1	0.00418
Seven Hills, but really Remote	1	0.00418
Sewaren	1	0.00418
Sewickley	1	0.00418
Seymour	1	0.00418
sf	1	0.00418
Sf	1	0.00418
SF Bay	1	0.00418
Shaker Heights	1	0.00418
Shaker Hts	1	0.00418
Shakopee	1	0.00418
Shanghai	1	0.00418
Sharon	1	0.00418
Shawnee Mission	1	0.00418
Sheboygan, WI	1	0.00418
Shelby	1	0.00418
Shelter Island Heights	1	0.00418
Sherborne	1	0.00418
Sherbrooke	1	0.00418
Sherman	1	0.00418
Sherman Texas	1	0.00418
Shingle Springs	1	0.00418
SHIRLEY	1	0.00418
Shoreham-by-Sea	1	0.00418
Shoreline	1	0.00418
Shorewood	1	0.00418
Shrewsbury, VT	1	0.00418
Shropshire	1	0.00418
Sidney	1	0.00418
Siloam Springs	1	0.00418
silver spring	1	0.00418
Silver spring	1	0.00418
Silver Spring, Maryland	1	0.00418
Silver Spring, MD	1	0.00418
Silver Spriung	1	0.00418
Silverton	1	0.00418
Simsbury	1	0.00418
singapore	1	0.00418
Singleton	1	0.00418
Sint-Niklaas	1	0.00418
Sioux City	1	0.00418
Skillman	1	0.00418
Skip	1	0.00418
Skipton	1	0.00418
Skokie	1	0.00418
Skowhegan	1	0.00418
Slave lake	1	0.00418
SLC	1	0.00418
Sliedrecht	1	0.00418
Slough	1	0.00418
Small Canadian City	1	0.00418
Small city	1	0.00418
Small City	1	0.00418
Small city in SW UK	1	0.00418
Small city--actual city would give away the company	1	0.00418
Small coastal town	1	0.00418
small college town	1	0.00418
Small country, prefer not to say!	1	0.00418
Small Rural Town. Would no longer be anonymous if stated.	1	0.00418
Small state - no answer	1	0.00418
Small town called Reigate, in Surrey just outside London	1	0.00418
Small town Ohio	1	0.00418
Small Town Ohio	1	0.00418
Small town, Cornwall	1	0.00418
Smithers	1	0.00418
Smiths Station	1	0.00418
Smyrna	1	0.00418
Snohomish	1	0.00418
Snoqualmie	1	0.00418
SoCal, prefer not to specify	1	0.00418
Somerset County	1	0.00418
Somerville /Cambridge	1	0.00418
Sonoma	1	0.00418
Sonoma County	1	0.00418
Souderton	1	0.00418
South	1	0.00418
South bend	1	0.00418
south central AK	1	0.00418
South Charleston	1	0.00418
South Elgin	1	0.00418
south jordan	1	0.00418
South Molton	1	0.00418
South Orange	1	0.00418
South portland	1	0.00418
South Texas	1	0.00418
South Yarmouth	1	0.00418
Southeast WI	1	0.00418
SOUTHEND-ON-SEA	1	0.00418
Southern CA	1	0.00418
Southern Denmark	1	0.00418
southern IL	1	0.00418
Southern Maine	1	0.00418
Southern Maryland	1	0.00418
Southern MN	1	0.00418
Southington	1	0.00418
Southwest MI	1	0.00418
Sparks	1	0.00418
Spoka	1	0.00418
Spring	1	0.00418
Springdale	1	0.00418
Springfield, IL	1	0.00418
Springfield, ma	1	0.00418
Springfield, MA	1	0.00418
St Andrews, Scotland	1	0.00418
St Anne's	1	0.00418
St Clair Shores	1	0.00418
St joe	1	0.00418
St John's	1	0.00418
St John's, NL	1	0.00418
St John’s	1	0.00418
st louis	1	0.00418
St Louis, MO	1	0.00418
St Nazaire	1	0.00418
St pete beach	1	0.00418
St Petersburg	1	0.00418
St Thomas	1	0.00418
St. Catharines	1	0.00418
St. Charles	1	0.00418
St. Clair County	1	0.00418
St. Croix Falls	1	0.00418
St. Davids	1	0.00418
St. George	1	0.00418
St. John's, NL	1	0.00418
St. John’s	1	0.00418
St. Johns	1	0.00418
St. Joseph	1	0.00418
St. louis	1	0.00418
St. Louis adjacent	1	0.00418
St. Louis Park	1	0.00418
St. Paul/Minniapolis area	1	0.00418
St. Simons Island	1	0.00418
Stamford, CT	1	0.00418
Stanford, CA	1	0.00418
Stanwood	1	0.00418
State COllege	1	0.00418
Staten Island	1	0.00418
Statesboro	1	0.00418
Steamboat Springs	1	0.00418
Steinbach	1	0.00418
Steinbach, Manitoba	1	0.00418
Sterling Heights	1	0.00418
Stevenage	1	0.00418
Stillwater: WI based company, office in MN	1	0.00418
STL	1	0.00418
Stockholm. But also from Italy (online work). Or anywhere actually.	1	0.00418
Stockton, NJ	1	0.00418
Stoke	1	0.00418
Stoke on Trent	1	0.00418
Stone Mountain	1	0.00418
Stone, Staffordshire	1	0.00418
Stoughton, MA	1	0.00418
Strasbourg	1	0.00418
Stratford upon avon	1	0.00418
Stratford upon Avon	1	0.00418
Stratham	1	0.00418
Streamwood	1	0.00418
Sturbridge	1	0.00418
Sturtevant	1	0.00418
Suburb city in Metro NY area	1	0.00418
Suburb of Indianapolis	1	0.00418
Suburb of Philadelphia	1	0.00418
Suburban Chicago, Illinois	1	0.00418
Suburban Deteoit	1	0.00418
Suburban NYC Metro Area	1	0.00418
Suburban Philadelphia	1	0.00418
Suburban School District	1	0.00418
Suffolk County	1	0.00418
Sugar land	1	0.00418
Summerville	1	0.00418
Sumnyvale	1	0.00418
Sumter	1	0.00418
Sun Prairie	1	0.00418
Sun Valley	1	0.00418
Sunshine Coast	1	0.00418
Superior	1	0.00418
Surrey BC	1	0.00418
Surrey, BC	1	0.00418
Surrey, British Columbia	1	0.00418
Surrey, British Columbia, Canada	1	0.00418
Sutherlin	1	0.00418
Sutton (Surrey)	1	0.00418
SW MO - near Joplin	1	0.00418
Swampscott	1	0.00418
Sweet Springs	1	0.00418
Swift Current	1	0.00418
Swiftwater	1	0.00418
Sydney, NSW, Australia	1	0.00418
syracuse	1	0.00418
TACOMA	1	0.00418
Tacoma (we are now permanently WFH, but our office was based in Tacoma)	1	0.00418
Tahoe City	1	0.00418
Takoma Park, MD	1	0.00418
Tallahassee, FL	1	0.00418
Tallahssee	1	0.00418
Tallmadge	1	0.00418
Tampa Bay	1	0.00418
Tampa Bay area	1	0.00418
Tampa Bay region	1	0.00418
Tarrytown, NY	1	0.00418
Taunton	1	0.00418
Taylorsville	1	0.00418
Te Whanganui-a-Tara Wellington	1	0.00418
Tehachapi	1	0.00418
Tel aviv	1	0.00418
Telecommute	1	0.00418
Telecommute from Anaheim, CA; employer office in Rancho Cucamonga, CA	1	0.00418
Telecommute from small town	1	0.00418
Telework	1	0.00418
Telford	1	0.00418
Tell city	1	0.00418
Temecula	1	0.00418
Tempe, AZ	1	0.00418
Tennant Creek	1	0.00418
Tennessee	1	0.00418
Teramo	1	0.00418
Terrell	1	0.00418
Tewksbury	1	0.00418
Texas	1	0.00418
Texas City	1	0.00418
Thame	1	0.00418
Thayne	1	0.00418
The Dalles, Oregon	1	0.00418
The hague	1	0.00418
The Hague area	1	0.00418
the office is in Schaumburg; I work remotely	1	0.00418
The Pas Manitoba	1	0.00418
This answer would reveal my identity.	1	0.00418
This identifies my employer	1	0.00418
This info will make me identifiable	1	0.00418
This is too identifying	1	0.00418
This would definitely reveal my identity	1	0.00418
This would help identify me	1	0.00418
This would narrow things down too much, sorry!	1	0.00418
This would probably out me	1	0.00418
Thorofare	1	0.00418
Thousand oaks	1	0.00418
Three Rivers	1	0.00418
Tifton	1	0.00418
Tilton	1	0.00418
Timaru	1	0.00418
Tinley Park	1	0.00418
Tinton Falls	1	0.00418
Tishomingo	1	0.00418
Tiverton (Remote) but company based in Washington	1	0.00418
To Much Detail	1	0.00418
toledo	1	0.00418
Too identifiable	1	0.00418
Too Small	1	0.00418
too small to answer with anonymity!	1	0.00418
Too small to list	1	0.00418
Tooeka	1	0.00418
Torino	1	0.00418
Tornto	1	0.00418
Toronto ON	1	0.00418
Toronto Ontario Canada	1	0.00418
Toronto, for a global remote company	1	0.00418
Toronto, ON, CA	1	0.00418
Toronto, ontario	1	0.00418
Toronto, Ontario, Canada	1	0.00418
Torontoo	1	0.00418
Torquay, England	1	0.00418
Torrance, CA	1	0.00418
Torre pacheco	1	0.00418
Toulouse	1	0.00418
Traveling	1	0.00418
Traveling over all of Europe	1	0.00418
Trinidad	1	0.00418
Trondheim	1	0.00418
Truth or Consequences	1	0.00418
tualatin	1	0.00418
Tucker	1	0.00418
tucson	1	0.00418
Tucson, AZ	1	0.00418
Tucson, but I'm remote to a CO company	1	0.00418
Tukwila	1	0.00418
Tullahoma	1	0.00418
Tuolumne	1	0.00418
Turin	1	0.00418
Turin/Torino	1	0.00418
Turku	1	0.00418
Tustin but represent Orange County	1	0.00418
Twin cities	1	0.00418
Twin Cities (state wide org)	1	0.00418
Twin Cities Area	1	0.00418
Twin cities metro area	1	0.00418
Twin Cities metro area	1	0.00418
Twin Cities Metro Area	1	0.00418
Tyson's Corner	1	0.00418
Tysons corner	1	0.00418
Tysons Corner	1	0.00418
Uckfield	1	0.00418
Uedem	1	0.00418
Uintah	1	0.00418
uk	1	0.00418
Uk	1	0.00418
Ukiah	1	0.00418
Ullin	1	0.00418
Unicoi	1	0.00418
Union	1	0.00418
Union, NJ	1	0.00418
Uniondale	1	0.00418
United Kingdom	1	0.00418
Universal city	1	0.00418
University City	1	0.00418
University Park	1	0.00418
Upper Penninsula	1	0.00418
Upper valley	1	0.00418
Upper Valley	1	0.00418
Uppsala	1	0.00418
upstate	1	0.00418
Upstate	1	0.00418
Upstate New York	1	0.00418
Upstate region	1	0.00418
Upstate, NY	1	0.00418
US govt employee overseas, country withheld	1	0.00418
USA	1	0.00418
Utah	1	0.00418
Utah County (I cover multiple cities)	1	0.00418
Uusimaa	1	0.00418
Uxbridge	1	0.00418
Vai	1	0.00418
Vail	1	0.00418
Valletta	1	0.00418
Vancouver area	1	0.00418
Vancouver, BC	1	0.00418
Vancouver, B.C.	1	0.00418
Vancouver, Canada	1	0.00418
Vancouver, WA	1	0.00418
Varies	1	0.00418
Variety	1	0.00418
Vega	1	0.00418
Venice	1	0.00418
Vernal	1	0.00418
Vernon - Remote position though	1	0.00418
Vernon hills	1	0.00418
VERONA	1	0.00418
Verysmall town	1	0.00418
victoria	1	0.00418
Victoria BC	1	0.00418
Victoria, B.C., Canada	1	0.00418
Victoria, BC, Canada	1	0.00418
Vienna, VA	1	0.00418
Villa Park	1	0.00418
village near Riga	1	0.00418
Vinalhaven	1	0.00418
Vincennes	1	0.00418
Vineland	1	0.00418
Vineland, NJ	1	0.00418
Virgina Beach	1	0.00418
Viroqua	1	0.00418
Virtual role	1	0.00418
Virtual Worker	1	0.00418
WA	1	0.00418
Waddell	1	0.00418
Waikato	1	0.00418
Wailuku	1	0.00418
Waitsfield	1	0.00418
Wake Forest	1	0.00418
walla walla	1	0.00418
Wallingford	1	0.00418
Walnut Creek (SF Bay Area)	1	0.00418
Walnut Creek, California	1	0.00418
Walpole	1	0.00418
Waltham (suburb of Boston)	1	0.00418
Waltham, MA	1	0.00418
Warren, NJ	1	0.00418
Warrenton	1	0.00418
Warwick, RI	1	0.00418
WASHINGTON	1	0.00418
washington DC	1	0.00418
Washington DC (and Philadelphia PA)	1	0.00418
Washington DC metro area	1	0.00418
Washington state	1	0.00418
Washington, DC	1	0.00418
Washington, d.c.	1	0.00418
Washington, dc	1	0.00418
Washington, DC - Baltimore, MD area.	1	0.00418
Washington, DC (I'm remote, but office is in DC)	1	0.00418
Washington, DC area	1	0.00418
Washington, DC Primarily	1	0.00418
Washington, DC.	1	0.00418
Washington, District of Columbia	1	0.00418
WashingtonDC	1	0.00418
Wasington	1	0.00418
Waskesiu Lake	1	0.00418
Waterbury	1	0.00418
WATERBURY	1	0.00418
Waterbury, VT	1	0.00418
Waterford, MI	1	0.00418
Waterloo, ON, Canada	1	0.00418
Watertown (Greater Boston area)	1	0.00418
Watertown, MA	1	0.00418
Watsonville	1	0.00418
Wauconda	1	0.00418
Waukegan	1	0.00418
Wausau	1	0.00418
Wauseon	1	0.00418
Waverly	1	0.00418
Wayland	1	0.00418
Wayne, PA	1	0.00418
waynesboro	1	0.00418
Weaverville	1	0.00418
Weed, CA	1	0.00418
Weert	1	0.00418
Welland	1	0.00418
Wellesley	1	0.00418
Wellsboro	1	0.00418
Welwyn Garden City	1	0.00418
Wentzville	1	0.00418
Wentzville but I am remote	1	0.00418
West Babylon	1	0.00418
West Bridgewater	1	0.00418
West Cola	1	0.00418
West Conshohocken	1	0.00418
West England	1	0.00418
West Greenwich	1	0.00418
West Grove	1	0.00418
West Hollywood	1	0.00418
West Mifflin	1	0.00418
West Orange	1	0.00418
West Palm Beaxh	1	0.00418
West Springfield	1	0.00418
West St Paul	1	0.00418
West St. Paul	1	0.00418
West Sussex	1	0.00418
west TN	1	0.00418
West TN	1	0.00418
West Warwick	1	0.00418
Westampton	1	0.00418
Westchester/NYC	1	0.00418
Westerly	1	0.00418
Western Canada	1	0.00418
Western KS	1	0.00418
Western MA	1	0.00418
Western Mass	1	0.00418
Western MD	1	0.00418
Western Slope	1	0.00418
Westerville	1	0.00418
Westford	1	0.00418
Westlake Village, CA	1	0.00418
Westport	1	0.00418
WFH (Was Topeka, KS. Now Whiting, KS)	1	0.00418
WFH FT	1	0.00418
WFH in Northern NJ but company HQ is in Illinois, local office NYC	1	0.00418
WFH in San Francisco Bay Area	1	0.00418
WFH Kenilworth	1	0.00418
WFH remotely, employer in another state	1	0.00418
WFH. Job located in Newport, South Wales	1	0.00418
Wheatfield	1	0.00418
Whistler	1	0.00418
Whistler, BC	1	0.00418
White Earth	1	0.00418
White Marsh	1	0.00418
White Oak	1	0.00418
White River Junction, VT	1	0.00418
Whitefish Bay	1	0.00418
Whitehorse, YT	1	0.00418
Wichita (remotely)	1	0.00418
Wichita Falls	1	0.00418
Wilbraham	1	0.00418
Wilkes-Barre	1	0.00418
Willamette Valley Area	1	0.00418
Williamsburg, VA	1	0.00418
Williamsport	1	0.00418
Williamston	1	0.00418
Williamstown	1	0.00418
Willimantic	1	0.00418
Williston	1	0.00418
Willowbrook	1	0.00418
wilmington	1	0.00418
wilmington, nc	1	0.00418
Wilmington/ WFH	1	0.00418
Wilmslow	1	0.00418
Wilton, Connecticut	1	0.00418
Wiltshire	1	0.00418
Winchester, UK	1	0.00418
Windham	1	0.00418
Windsor Ontario	1	0.00418
Winkler	1	0.00418
Winter Park	1	0.00418
Winton	1	0.00418
Wisconsin	1	0.00418
Witchita	1	0.00418
Wixom	1	0.00418
Woburn (Greater Boston area; I provide services in a number of towns.)	1	0.00418
Woking	1	0.00418
Wokingham	1	0.00418
Wollongong	1	0.00418
Wolverhampton	1	0.00418
Wood Dale	1	0.00418
woodbridge	1	0.00418
Woodbridge	1	0.00418
Woodhaven	1	0.00418
Woodland	1	0.00418
Woodland Hills	1	0.00418
Woods Hole	1	0.00418
Woonsocket	1	0.00418
Wooster	1	0.00418
Worcester area	1	0.00418
Work across the whole state	1	0.00418
Work from Home	1	0.00418
Work From Home	1	0.00418
Work from home, London	1	0.00418
Work remotely for a NYC business from Portland, ME	1	0.00418
Work remotely, but the company is based out of Chicago, IL and I work in Newark, NJ	1	0.00418
Worthington	1	0.00418
would be identifying sorry	1	0.00418
would give too much away	1	0.00418
Would rather not answer ( would be very easy to figure out who i am)	1	0.00418
would rather not say	1	0.00418
would remove my anonymity to state this	1	0.00418
Wrexham	1	0.00418
Wright	1	0.00418
Würzburg	1	0.00418
Wyandanch	1	0.00418
Wyandotte	1	0.00418
Yakima	1	0.00418
Yakima but my employer is based in Seattle	1	0.00418
Yankton	1	0.00418
Yellowknife, NT	1	0.00418
Yeovil	1	0.00418
Yonkers	1	0.00418
York Region	1	0.00418
Yorkshire	1	0.00418
Youngstown	1	0.00418
Zachary	1	0.00418
Zagreb	1	0.00418
Zion	1	0.00418
Zurich	1	0.00418

# education 
ask_a_manager_2021 %>% 
  count(education) %>% 
  mutate(percent = 100* n/sum(n))%>% 
  arrange(desc(percent))

education	n	percent
College degree	11665	48.7
Master's degree	7811	32.6
Some college	1707	7.13
PhD	1228	5.13
Professional degree (MD, JD, etc.)	1011	4.22
High School	507	2.12

# gender
ask_a_manager_2021 %>% 
  count(gender) %>% 
  mutate(percent = 100* n/sum(n))%>% 
  arrange(desc(percent))

gender	n	percent
Woman	19626	82
Man	4303	18

# race
ask_a_manager_2021 %>% 
  count(race) %>% 
  mutate(percent = 100* n/sum(n))%>% 
  arrange(desc(percent))

race	n	percent
White	20262	84.7
Asian or Asian American	1087	4.54
Black or African American	578	2.42
Hispanic, Latino, or Spanish origin	478	2
Another option not listed here or prefer not to answer	403	1.68
Hispanic, Latino, or Spanish origin, White	325	1.36
Asian or Asian American, White	293	1.22
Black or African American, White	108	0.451
Middle Eastern or Northern African, White	61	0.255
Native American or Alaska Native, White	60	0.251
White, Another option not listed here or prefer not to answer	51	0.213
Middle Eastern or Northern African	50	0.209
Native American or Alaska Native	34	0.142
Black or African American, Hispanic, Latino, or Spanish origin	24	0.1
Asian or Asian American, Hispanic, Latino, or Spanish origin	10	0.0418
Asian or Asian American, Hispanic, Latino, or Spanish origin, White	9	0.0376
Hispanic, Latino, or Spanish origin, Native American or Alaska Native	9	0.0376
Asian or Asian American, Another option not listed here or prefer not to answer	8	0.0334
Black or African American, Hispanic, Latino, or Spanish origin, White	8	0.0334
Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White	8	0.0334
Asian or Asian American, Black or African American	7	0.0293
Asian or Asian American, White, Another option not listed here or prefer not to answer	6	0.0251
Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer	6	0.0251
Asian or Asian American, Black or African American, White	5	0.0209
Asian or Asian American, Middle Eastern or Northern African	5	0.0209
Black or African American, Native American or Alaska Native, White	5	0.0209
Asian or Asian American, Native American or Alaska Native, White	4	0.0167
Black or African American, Middle Eastern or Northern African, White	4	0.0167
Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African, White	3	0.0125
Black or African American, Another option not listed here or prefer not to answer	2	0.00836
Black or African American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White	2	0.00836
Black or African American, Middle Eastern or Northern African	2	0.00836
Middle Eastern or Northern African, White, Another option not listed here or prefer not to answer	2	0.00836
Asian or Asian American, Hispanic, Latino, or Spanish origin, Another option not listed here or prefer not to answer	1	0.00418
Asian or Asian American, Hispanic, Latino, or Spanish origin, Native American or Alaska Native, White	1	0.00418
Asian or Asian American, Middle Eastern or Northern African, White	1	0.00418
Black or African American, Middle Eastern or Northern African, Native American or Alaska Native, White	1	0.00418
Hispanic, Latino, or Spanish origin, Middle Eastern or Northern African	1	0.00418
Hispanic, Latino, or Spanish origin, Native American or Alaska Native, Another option not listed here or prefer not to answer	1	0.00418
Middle Eastern or Northern African, Native American or Alaska Native	1	0.00418
Middle Eastern or Northern African, Native American or Alaska Native, White	1	0.00418
Native American or Alaska Native, Another option not listed here or prefer not to answer	1	0.00418
Native American or Alaska Native, White, Another option not listed here or prefer not to answer	1	0.00418

# pro_exp 
ask_a_manager_2021 %>% 
  count(pro_exp ) %>% 
  mutate(percent = 100* n/sum(n))%>% 
  arrange(desc(percent))

pro_exp	n	percent
11 - 20 years	8332	34.8
8 - 10 years	4627	19.3
5-7 years	4128	17.3
21 - 30 years	3123	13.1
2 - 4 years	2473	10.3
31 - 40 years	745	3.11
1 year or less	392	1.64
41 years or more	109	0.456

# field_exp  
ask_a_manager_2021 %>% 
  count(field_exp) %>% 
  mutate(percent = 100* n/sum(n))%>% 
  arrange(desc(percent))

field_exp	n	percent
5-7 years	5657	23.6
11 - 20 years	5642	23.6
2 - 4 years	5235	21.9
8 - 10 years	4315	18
21 - 30 years	1561	6.52
1 year or less	1166	4.87
31 - 40 years	319	1.33
41 years or more	34	0.142

#make comments about the counts 

Salary (Yuan)

How is salary distributed?

favstats(ask_a_manager_2021$salary) # find a really rich guy here

======= mutate(percent = 100* n/sum(n)) # education ask_a_manager_2021 %>% count(education) %>% mutate(percent = 100* n/sum(n))%>% arrange(desc(percent)) # gender ask_a_manager_2021 %>% count(gender) %>% mutate(percent = 100* n/sum(n))%>% arrange(desc(percent)) # race ask_a_manager_2021 %>% count(race) %>% mutate(percent = 100* n/sum(n))%>% arrange(desc(percent)) # pro_exp ask_a_manager_2021 %>% count(pro_exp ) %>% mutate(percent = 100* n/sum(n))%>% arrange(desc(percent)) # field_exp ask_a_manager_2021 %>% count(field_exp ) %>% mutate(percent = 100* n/sum(n))%>% arrange(desc(percent)) #make comments about the counts

Salary

How is salary distributed?

favstats(ask_a_manager_2021$salary) # find a really rich guy here

min	Q1	median	Q3	max	mean	sd	n	missing
1	5.4e+04	7.5e+04	1.04e+05	1.92e+05	8.18e+04	3.75e+04	23929	0

>>>>>>> XuejingHuang-master

min	Q1	median	Q3	max	mean	sd	n	missing
0	5.5e+04	7.6e+04	1.09e+05	5e+06	8.93e+04	7.07e+04	24899	0

# density
g1 <- ggplot(ask_a_manager_2021, aes(x=salary))+
  geom_density()+
  NULL
#cdf
g2 <- ask_a_manager_2021 %>% 
  slice_min(order_by = salary,n =nrow(ask_a_manager_2021)-5) %>% # choose a number
  ggplot(aes(x=salary))+
  stat_ecdf()+
  NULL

# taking log is better
g3 <- ggplot(ask_a_manager_2021, aes(x=log(salary)))+
  geom_density()+
  NULL

g4 <- ask_a_manager_2021 %>% 
  slice_min(order_by = salary,n =nrow(ask_a_manager_2021)-5) %>% # choose a number
  ggplot(aes(x=log(salary)))+
  stat_ecdf()+
  NULL

gridExtra::grid.arrange(g1,g2,g3,g4)

<<<<<<< HEAD

Observation: This is a right skewed distribution.

Industry (Yuan)

=======

Observation: This is a right skewed distribution.

Industry

>>>>>>> XuejingHuang-master

ask_a_manager_2021 %>% 
  count(industry, sort=TRUE) %>% 
  ggplot(aes(y=fct_reorder(industry,n), x=n))+
  geom_col()

<<<<<<< HEAD

=======

>>>>>>> XuejingHuang-master

Observations:

• Computing & tech is the top-chosen industry among respondents, followed by Education and Non-profit
• This indicate that the sample might include more respondents from industries have higher exposure to tech and internet.

<<<<<<< HEAD

Salary ,Gender and Industry (Yuan)

=======

Salary ,Gender and Industry

>>>>>>> XuejingHuang-master

We are interested in the type of industries with top salary for man and woman.

# Industries of top average salary for Man and Woman
salary_table <- ask_a_manager_2021 %>% 
  group_by(industry, gender) %>% 
  summarise(count = n(),
            avg_salary = mean(converted_salary),
            higher_edu_prop = sum(higher_edu)/n()) %>% 
  pivot_wider(id_cols = 1:5,
              names_from = gender,
              values_from = count:higher_edu_prop)
  
g_man <- salary_table %>% 
  ggplot(aes(y=fct_reorder(industry,avg_salary_Man), x=avg_salary_Man,fill=higher_edu_prop_Man))+
  geom_bar(position="dodge", stat="identity")+
  geom_text(aes(label=paste(avg_salary_Man%/%1000,"k")),
            position=position_dodge(width=0.9), hjust=0,vjust=0)+
  scale_x_continuous(labels = scales::comma) +
  scale_fill_gradient(low="sky blue", high="blue")+
  labs(
    title="Man",
    x="Annual Salary $",
    y="Industry",
    fill="Higher Education %"
  )+
  theme_wsj()+
  theme(legend.position="bottom",
        legend.title=element_text(size=13))+
  NULL

<<<<<<< HEAD
ggsave("highest_paid_industries_male.png",width = 12,height = 8,limitsize = F)
=======
# ggsave("highest_paid_industries_male.png",width = 12,height = 8,limitsize = F)

>>>>>>> XuejingHuang-master
g_woman <- salary_table %>% 
  ggplot(aes(y=fct_reorder(industry,avg_salary_Woman), x=avg_salary_Woman,fill=higher_edu_prop_Woman))+
  geom_bar( position="dodge", stat="identity")+
  geom_text(aes(label=paste(avg_salary_Woman%/%1000,"k")),
              position=position_dodge(width=0.9), hjust=0,vjust=0)+
  scale_x_continuous(labels = scales::comma) +
  scale_fill_gradient(low="pink", high="red")+
  labs(
    title="Woman",
    x="Annual Salary $",
    y="Industry",
    fill="Higher Education %"
  )+
  theme_wsj()+
  theme(legend.position="bottom",
        legend.title=element_text(size=13))+
  NULL

<<<<<<< HEAD

ggsave("highest_paid_industries_female.png",width = 12,height = 8,limitsize = F)

======= # ggsave("highest_paid_industries_female.png",width = 12,height = 8,limitsize = F) >>>>>>> XuejingHuang-master

Observations:

• For both man and woman, the top earning industries are alike (Law, Computing or Tech, Business or Consulting)
• Respondents from Law, Govenment and Education tends to have higher education level compared to the rest, reflecting the academic/professional requirements of these positions
• The difference between the salary within sales and accounting, banking & finance is likely due to the subdivisions chosen based on gender nature

We are also interested in the salary difference between man and woman within the same industry.

# salary gap in different industries
ask_a_manager_2021 %>% 
  group_by(industry, gender) %>% 
  summarise(count = n(),
            avg_salary = mean(converted_salary)) %>% 
  pivot_wider(id_cols = 1:4,
              names_from = gender,
              values_from = count:avg_salary) %>% 
  mutate(size = count_Man+count_Woman,
         male_prop = count_Man/(count_Man+count_Woman),
         salary_gap = avg_salary_Man - avg_salary_Woman) %>% 
  ggplot(aes(y=fct_reorder(industry,salary_gap), x=salary_gap,fill=male_prop))+
  geom_col()+
  
  # geom_point(aes(y=fct_reorder(industry,size),x=size*10))+
  
  scale_x_continuous(
    breaks = seq(-20000,40000,5000)
  ) +
  scale_fill_gradient(low="pink", high="blue")+
  labs(
    title="Salary Gap and Sex Ratio",
    x="Salary Gap in $",
    y="",
    fill="Male %"
  )+
  theme_wsj()+
  theme(legend.position="bottom",
        legend.title=element_text(size=13))+
  NULL

<<<<<<< HEAD

ggsave("salary_gap.png",width = 12,height = 8,limitsize = F)

=======

# ggsave("salary_gap.png",width = 12,height = 8,limitsize = F)

>>>>>>> XuejingHuang-master

Observations:

• Man has a higher average salary than woman in most of the industries, with Law being the most significant
• Woman has higher average salary in Art & Design
• The proportion of male within computing or tech is significantly higher than that within other industries; yet the proportion only reaches c. 40%, reflecting the fact that most respondents are female because Ask_a_manager is more popular among women
• The proportion of male is also higher in labour-intensive industries such as transport or logistics, engineering, utilities & telecommunications

<<<<<<< HEAD

Education (Jean)

=======

Education

>>>>>>> XuejingHuang-master

Next, we want to see if there is a difference in the salary gap between man and woman for different education level.

ask_a_manager_2021 %>% 
  group_by(education, gender) %>% 
  summarise(count = n(),
            avg_salary = mean(converted_salary)) %>% 
  pivot_wider(id_cols = 1:4,
              names_from = gender,
              values_from = count:avg_salary) %>% 
  mutate(size = count_Man+count_Woman,
         male_prop = count_Man/(count_Man+count_Woman),
         salary_gap = avg_salary_Man - avg_salary_Woman) %>% 
  ggplot(aes(x=education, y=salary_gap,fill=male_prop))+
  geom_col()+
  scale_y_continuous(
    breaks = seq(-20000,40000,5000)
  ) +
  scale_fill_gradient(low="pink", high="sky blue")+
  theme_wsj()+
  theme(legend.position="bottom", axis.title=element_text(size=24))+
  ggtitle("Salary Gap at Different Education level") +
  xlab("Education Level") + ylab ("Salary Gap in $") + 
  NULL

<<<<<<< HEAD

ggsave("salary_gap_by_edu.png",width = 14,height = 8,limitsize = F)

Race (Clara)

=======

# ggsave("salary_gap_by_edu.png",width = 14,height = 8,limitsize = F)

Race

>>>>>>> XuejingHuang-master

Count

ask_a_manager_2021 %>% 
  group_by(race) %>% 
  filter(n() > 500) %>% 
  summarise(count=n()) %>% 
  ggplot(aes(y=count, x=race, fill=race))+
  geom_col() +
  theme_wsj()+
  theme(legend.position = "none") +
  labs(x="Race", y="Count", title="Count by Race", subtitle = "Races with > 500 Respondents")+
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) + 
  NULL

<<<<<<< HEAD

=======

>>>>>>> XuejingHuang-master

Race and Mean Salary

ask_a_manager_2021 %>% 
  filter(industry %in% industry_list$industry) %>% 
  group_by(race) %>% 
  filter(n() > 500) %>% 
 ggplot( aes(x=race, y=converted_salary, color=race)) +
    geom_jitter(color="black", size=0.01, alpha=0.9) +
   geom_boxplot(alpha=0.4) +
   scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    geom_hline(yintercept=144988, color="red") +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    theme_wsj() +
    theme(legend.position = "none")+
    theme(axis.title=element_text(size=10))+
    ggtitle("Salary Vs. Race Boxplot") +
    ylab("Salary") + xlab ("Race") +
    scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
    scale_y_continuous(labels = scales::comma, limits = c(0, 300000)) +
    NULL

<<<<<<< HEAD

Observation: In our boxplot one can visualize both the distribution of the data and the medium and interquartile ranges of the data. When comparing annual salary by race, our data shows that Asians have the higher salaries in comparison to Black and African American and White sample respondents. Interestingly enough, our sample also shows that the medium salary for Black and AA respondents is higher than that of White respondents.

Since most repondents were white, the larger sample size is less prone to skewness due to outliers when comparing it to the other two races portrayed in the plot.

=======

Observation:

• In our boxplot one can visualize both the distribution of the data and the medium and interquartile ranges of the data. When comparing annual salary by race, our data shows that Asians have the higher salaries in comparison to Black and African American and White sample respondents. Interestingly enough, our sample also shows that the medium salary for Black and AA respondents is higher than that of White respondents.

• Since most repondents were white, the larger sample size is less prone to skewness due to outliers when comparing it to the other two races portrayed in the plot.

>>>>>>> XuejingHuang-master

ask_a_manager_2021_race <- ask_a_manager_2021 %>%
  group_by(race) %>% 
  filter(race %in% c("Black or African American", "White")) 

t.test(salary ~ race, data = ask_a_manager_2021_race)

## 
##  Welch Two Sample t-test
## 
## data:  salary by race
<<<<<<< HEAD
## t = 0.6, df = 610, p-value = 0.5
## alternative hypothesis: true difference in means between group Black or African American and group White is not equal to 0
## 95 percent confidence interval:
##  -2140  4021
## sample estimates:
## mean in group Black or African American                     mean in group White 
##                                   81976                                   81035

When running a T-test on the two racial groups, White and Black or African American, the P-value we get is 0.9. Thus there is no statistically signifcant difference in their annual salary when calculating it based of off this data set. ### Race and Mean Salary

Location (Alex)

#create df of the list of states with >10 occurrences 
=======
## t = 1, df = 605, p-value = 0.3
## alternative hypothesis: true difference in means between group Black or African American and group White is not equal to 0
## 95 percent confidence interval:
##  -6923 26250
## sample estimates:
## mean in group Black or African American                     mean in group White 
##                                   97570                                   87906

When running a T-test on the two racial groups, White and Black or African American, the P-value we get is 0.9. Thus there is no statistically signifcant difference in their annual salary when calculating it based of off this data set.

Race and Mean Salary

Location

#This package is useful for creating a map of the USA
library(usmap)
library(ggrepel)

#create df of the list of states with >10 occurrences 
>>>>>>> XuejingHuang-master
#This allows us to get data for actual states as many data entry errors
#many individuals put multiple states and this helps to fix that
states <- ask_a_manager_2021 %>% 
  group_by(state) %>%
  filter(!is.na(state)) %>%
  filter(n()>10) %>% 
  summarise(mean_salary = mean(salary),
            count_state = count(state))

states$fips <- fips(states$state)
statesalarymap <- states %>% select(state,mean_salary)

plot_usmap(data=statesalarymap, values="mean_salary", color="black") + scale_fill_continuous(
    low = "white",
    high = "light green",
    name = "Mean Salary") +
  theme(legend.position = "right") +
  labs(title="Map of US States based on Average Salary")

<<<<<<< HEAD

=======

>>>>>>> XuejingHuang-master

MAP

# Count by Industry

ask_a_manager_2021 %>% 
  group_by(industry) %>% 
  summarise(count=n()) %>% 
  ggplot(aes(y=fct_reorder(industry,count),x=count))+
  geom_col()

<<<<<<< HEAD

# null hypothesis: no difference between

=======

# null hypothesis: no difference between

Age

ask_a_manager_2021 %>% 
  group_by(age_group, gender) %>% 
  summarise(count=n()) 

## # A tibble: 13 x 3
## # Groups:   age_group [7]
##    age_group  gender count
##    <fct>      <chr>  <int>
##  1 under 18   Woman      6
##  2 18-24      Man      168
##  3 18-24      Woman    759
##  4 25-34      Man     1853
##  5 25-34      Woman   9155
##  6 35-44      Man     1907
##  7 35-44      Woman   7110
##  8 45-54      Man      616
##  9 45-54      Woman   2327
## 10 55-64      Man      156
## 11 55-64      Woman    754
## 12 65 or over Man       17
## 13 65 or over Woman     71

  #ggplot(aes(y=fct_reorder(how_old_are_you,count),x=count))+
  #geom_col()

>>>>>>> XuejingHuang-master

# Count by Age Group
ask_a_manager_2021 %>% 
  group_by(age_group) %>% 
  summarise(count=n()) %>% 
  ggplot(aes(y=fct_reorder(age_group,count),x=count))+
  geom_col()

<<<<<<< HEAD

=======

>>>>>>> XuejingHuang-master

# Sorting out the people who are 18+ 
ask_a_manager_age <- ask_a_manager_2021 %>%  
                    mutate(factor(age_group,levels=c("18-24","25-34","35-44","45-54","55-64","65 or over")))

#need to change colors for male and female ?!
#there are only 6 women in under 18 category so excluding them for the graph
# since there is no male data in that category to compare
ask_a_manager_age %>% 
  filter(age_group %in% c("18-24", "25-34" , "35-44" , "45-54" , "55-64"  )) %>% 
  group_by(age_group, gender) %>% 
  summarise (median_salary_by_age = median (salary),
             mean_salary_by_age = mean(salary)) %>% 
  ggplot () +
  geom_col(aes (age_group, median_salary_by_age, fill = gender), position = position_dodge(width = 0.9), alpha = 0.7)+
  geom_point(aes(x = age_group, y=mean_salary_by_age, color = gender))+
  theme_bw()+
  #formatC(x, format = "d")+
  labs(  title =  "Median Salary by Age Group" , y="Salary" ,x = "Age Group") +
  NULL

<<<<<<< HEAD

=======

Changed Industry vs salary

ask_a_manager_2021_changed <- ask_a_manager_2021  %>%
mutate(if_changed= if_else(overall_years_of_professional_experience == years_of_experience_in_field, "No", "Yes"))

ask_a_manager_2021_changed %>% 
ggplot(aes(x=fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), y=annual_salary, color=if_changed))+
  # scatter plot
  geom_point(alpha = 0.5)+
  # linear smooth line
  geom_smooth(method = "lm")+
  # legend settings
  theme(legend.position = "bottom",
        legend.title = element_blank())+
  labs(title="Whether changing job affects your salary",
       x="Total Years Spent Working",
       y="Salary")+
  theme_bw()+
  NULL

ask_a_manager_2021_changed %>% 
  group_by(overall_years_of_professional_experience, if_changed) %>% 
  summarise (median_salary_over_time = median (salary),
             mean_salary_over_time = mean(salary)) %>% 
  ggplot () +
  geom_col(aes (fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), median_salary_over_time, fill=if_changed), position = position_dodge(width = 0.9), alpha = 0.7)+
  geom_point(aes(x = overall_years_of_professional_experience, y=mean_salary_over_time, color = if_changed))+
  theme_bw()+
  #formatC(x, format = "d")+
  labs(  title =  "Median Salary over time" , y="Salary" ,x = "Prof") +
  NULL

ggsave("switch_industry.png",width = 12,height = 8,limitsize = F)

>>>>>>> XuejingHuang-master

Statistical Tests

<<<<<<< HEAD

Gender vs Salary (Clara)

General Statistics & Boxplot

mosaic::favstats (converted_salary ~ gender, data=ask_a_manager_2021)

=======

Gender vs Salary

General Statistics & Boxplot

mosaic::favstats (converted_salary ~ gender, data=ask_a_manager_2021)

gender	min	Q1	median	Q3	max	mean	sd	n	missing
Man	1	6.3e+04	9e+04	1.28e+05	2.62e+05	9.67e+04	4.3e+04	4303	0
Woman	35	5.25e+04	7.11e+04	9.7e+04	2.55e+05	7.82e+04	3.48e+04	19626	0

>>>>>>> XuejingHuang-master

gender	min	Q1	median	Q3	max	mean	sd	n	missing
Man	0	6.5e+04	9.6e+04	1.41e+05	2.11e+06	1.12e+05	8.33e+04	4717	0
Woman	0	5.3e+04	7.2e+04	1e+05	5e+06	8.38e+04	6.63e+04	20182	0

ask_a_manager_2021 %>% 
  ggplot( aes(x=gender, y=converted_salary, color=gender)) +
    geom_jitter(color="black", size=0.4, alpha=0.9) +
    geom_boxplot(alpha=0.3) +
    scale_fill_viridis(discrete = TRUE, alpha=0.6) +
    theme_wsj() +
    theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
   theme(
      legend.position="none",
      plot.title = element_text(size=11)
    ) +
    theme(axis.title=element_text(size=12))+
    ggtitle("Salary Vs. Gender Boxplot with Jitter") +
    ylab("Salary") + xlab ("Race") + 
    scale_x_discrete(labels = function(x) str_wrap(x, width = 10))+
    scale_y_continuous(labels = scales::comma, limits = c(0, 1000000)) +
    NULL

<<<<<<< HEAD

=======

>>>>>>> XuejingHuang-master

For our following analysis, we will be comparing data from the male gender “Man” with data from the female gender “Woman.” We will filter out the data on those that are non binary.

T-Test

ask_a_manager_2021 <- ask_a_manager_2021 %>%
  filter(gender!="Non-binary")
t.test(converted_salary ~ gender, data = ask_a_manager_2021)

## 
##  Welch Two Sample t-test
## 
## data:  converted_salary by gender
<<<<<<< HEAD
## t = 26, df = 5602, p-value <2e-16
## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0
## 95 percent confidence interval:
##  17124 19871
## sample estimates:
##   mean in group Man mean in group Woman 
##               96721               78223

======= ## t = 21, df = 6183, p-value <2e-16 ## alternative hypothesis: true difference in means between group Man and group Woman is not equal to 0 ## 95 percent confidence interval: ## 25223 30318 ## sample estimates: ## mean in group Man mean in group Woman ## 111551 83781 >>>>>>> XuejingHuang-master

Confidence Intervals

#mosaic::favstats (annual_salary ~ gender, data=ask_a_manager_2021)
ask_a_manager_2021_gender <- ask_a_manager_2021 %>%
  mutate(gender2 = case_when(
  gender  == "Man" ~ "1",
 gender == "Woman" ~ "0",
  TRUE ~ "NA"
)) %>%
  mutate(gender2 = as.numeric(gender2))


mosaic::favstats (converted_salary ~ gender, data=ask_a_manager_2021_gender) %>%
mutate(t_critical = qt(0.975,n-1),
         sd_mean = sd/sqrt(n),
         margin_of_error = t_critical*sd_mean,
         ci_lower = mean-margin_of_error,
         ci_higher = mean+margin_of_error)

<<<<<<< HEAD

gender	min	Q1	median	Q3	max	mean	sd	n	missing	t_critical	sd_mean	margin_of_error	ci_lower	ci_higher
Man	1	6.3e+04	9e+04	1.28e+05	2.62e+05	9.67e+04	4.3e+04	4303	0	1.96	655	1.28e+03	9.54e+04	9.8e+04
Woman	35	5.25e+04	7.11e+04	9.7e+04	2.55e+05	7.82e+04	3.48e+04	19626	0	1.96	248	487	7.77e+04	7.87e+04

# Add graph of two distributions and confidence intervals 

Observation: Since two 95% confidence intervals do not overlap, we are at least 95% confident that the male have higher salary than the female on average.

======= Man06.5e+049.6e+041.41e+052.11e+061.12e+058.33e+04471701.961.21e+032.38e+031.09e+051.14e+05 Woman05.3e+047.2e+041e+05       5e+06       8.38e+046.63e+042018201.96466       914       8.29e+048.47e+04

# Add graph of two distributions and confidence intervals 

Observation:

• Since two 95% confidence intervals do not overlap, we are at least 95% confident that the male have higher salary than the female on average.

>>>>>>> XuejingHuang-master

Bootstrap Test

# hypothesis testing using infer package
diff <- ask_a_manager_2021_gender %>%
  specify(converted_salary ~ gender) %>%
  calculate(stat = "diff in means", order = c("Woman", "Man"))

set.seed(1234)
salary_in_null_world <- ask_a_manager_2021_gender %>%
  filter(gender !="Non-binary") %>%
  specify(converted_salary ~ gender) %>%
   # assuming independence of gender and salary
  hypothesize(null = "independence") %>%
   # generate 1000 reps, of type "permute"
  generate(reps = 1000, type = "permute") %>%
  # calculate statistic of difference, namely "diff in means"
   calculate(stat = "diff in means", order = c("Woman", "Man")) 

salary_in_null_world %>% 
  visualise()+
  shade_p_value(obs_stat = diff, direction = "two-sided")

<<<<<<< HEAD

p_value <- salary_in_null_world %>% 
  get_pvalue(obs_stat = diff, direction="both")
p_value

=======

p_value <- salary_in_null_world %>% 
  get_pvalue(obs_stat = diff, direction="both")
p_value

>>>>>>> XuejingHuang-master

p_value
0

<<<<<<< HEAD Observation: Our null hypothesis is that the difference of annual salaries between male and female is 0 and we get a p_value very close to 0. Therefore we reject the null hypothesis with 95% confidence. There is difference between the average male and female salary.

Gender vs Education (Jean)

=======

Observation:

• Our null hypothesis is that the difference of annual salaries between male and female is 0 and we get a p_value very close to 0. Therefore we reject the null hypothesis with 95% confidence. There is difference between the average male and female salary.

Gender vs Education

>>>>>>> XuejingHuang-master

We want to look at the relationship between one’s gender and education degree. In essence, we want to explore if there is a difference in the proportion of respondents with high school degree as their highest degree for male group and the female group. We can use the proportion test to generate the result from the male and female sample.

ask_a_manager_2021_education <- ask_a_manager_2021 %>% 
   filter(gender !="Non-binary") %>%
   mutate(below_college = if_else(highest_level_of_education_completed %in% c("High School", "Some college"), "Yes", "No")) %>% 
   group_by(gender) %>% 
   summarise(college_below=sum(below_college == "Yes"), college_or_above = sum(below_college == "No"))

ask_a_manager_2021_education

<<<<<<< HEAD ======= >>>>>>> XuejingHuang-master

gender	college_below	college_or_above
Man	635	3668
Woman	1579	18047
Man	687	4030
Woman	1598	18584

prop.test(x = c(4030,18584), n = c(4030+687,18584+1598), alternative = "two.sided")

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c out of c4030 out of 4030 + 68718584 out of 18584 + 1598
## X-squared = 202, df = 1, p-value <2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.0773 -0.0556
## sample estimates:
## prop 1 prop 2 
##  0.854  0.921

<<<<<<< HEAD

From the result of proportion test for male group and female group, we are 99% confident that the proportion of high school as highest education level for male is higher than that for female, indicating that overall female has a higher college-entry rate vs. the male.

=======

Observations:

• From the result of proportion test for male group and female group, we are 99% confident that the proportion of high school as highest education level for male is higher than that for female, indicating that overall female has a higher college-entry rate vs. the male.

>>>>>>> XuejingHuang-master

If we repeat the process to explore the difference in proportion in acquiring a masters or above degree for both gender:

ask_a_manager_2021_education <- ask_a_manager_2021 %>% 
  filter(gender !="Non-binary") %>%
  mutate(higher_edu = if_else(highest_level_of_education_completed %in% c("Master's degree", "Professional degree (MD, JD, etc.)", "PhD"), "Yes", "No")) %>% 
  group_by(gender) %>% 
  summarise(master_below = sum(higher_edu == "No"), master_or_above=sum(higher_edu == "Yes") )


ask_a_manager_2021_education

<<<<<<< HEAD ======= >>>>>>> XuejingHuang-master

gender	master_below	master_or_above
Man	2811	1492
Woman	11068	8558
Man	3063	1654
Woman	11260	8922

prop.test(x = c(1654, 8922), n = c(1654+3063, 8922+11260), alternative = "two.sided")

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c out of c1654 out of 1654 + 30638922 out of 8922 + 11260
## X-squared = 130, df = 1, p-value <2e-16
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.1068 -0.0761
## sample estimates:
## prop 1 prop 2 
##  0.351  0.442

<<<<<<< HEAD

From the result of two proportion tests, we can see that even though the proportion of acquiring a college-or-above degree is less in the male group, the proportion of acquiring higher education degree (Master’s-or-above) is higher than the female group. This somehow explains the fact that there are more outliers in male sample with extremely high income level.

Race vs Salary (Raina)

=======

Observations:

• From the result of two proportion tests, we can see that even though the proportion of acquiring a college-or-above degree is less in the male group, the proportion of acquiring higher education degree (Master’s-or-above) is higher than the female group
• This somehow explains the fact that there are more outliers in male sample with extremely high income level

Race vs Salary

>>>>>>> XuejingHuang-master

Here we want to see if race is affecting the salary.

ask_a_manager_salary <- ask_a_manager_2021 %>% 
  mutate(if_white = if_else(race == "White", "Yes", "No")) %>% 
  select(if_white,salary)
<<<<<<< HEAD
=======

>>>>>>> XuejingHuang-master
t.test(salary~if_white, data = ask_a_manager_salary, success = "Yes")

## 
##  Welch Two Sample t-test
## 
## data:  salary by if_white
<<<<<<< HEAD
## t = 7, df = 4948, p-value = 4e-12
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
##  3474 6195
## sample estimates:
##  mean in group No mean in group Yes 
##             85869             81035

wisely choose the race. like white vs. not white

Race vs Education (Jean)

======= ## t = 5, df = 4313, p-value = 6e-07 ## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0 ## 95 percent confidence interval: ## 5472 12543 ## sample estimates: ## mean in group No mean in group Yes ## 96913 87906

Race vs Education

>>>>>>> XuejingHuang-master

We want to look at the relationship between one’s race and education degree. Particularly, we want to see if there is a difference in education level for those being white and those being non-white.

As the above analysis for gender vs. education, we run two proportion tests for college-or-above degree percentage and master-or-above percentage between the male and female group.

ask_a_manager_2021_race <- ask_a_manager_2021 %>% 
  mutate(if_white = if_else(race == "White", "White", "Non-white"),
         below_college = if_else(highest_level_of_education_completed %in% c( "High School", "Some college"), "Yes", "No")) %>% 
  group_by(if_white) %>% 
  summarise(college_below=sum(below_college == "Yes"), college_or_above = sum(below_college == "No"))

ask_a_manager_2021_race

<<<<<<< HEAD ======= >>>>>>> XuejingHuang-master

if_white	college_below	college_or_above
Non-white	334	3333
White	1880	18382
Non-white	350	3510
White	1935	19104

prop.test(x = c(3510,19104), n = c(3510+350, 19104+1935), alternative = "two.sided")

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c out of c3510 out of 3510 + 35019104 out of 19104 + 1935
## X-squared = 0.05, df = 1, p-value = 0.8
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.00872  0.01132
## sample estimates:
## prop 1 prop 2 
##  0.909  0.908

Similarly, if we run the test for the propotion of master_or_above education level between white and non-white races:

ask_a_manager_2021_race <- ask_a_manager_2021 %>% 
  mutate(if_white = if_else(race == "White", "White", "Non-white"),
         higher_edu = if_else(highest_level_of_education_completed %in% c("Master's degree", "Professional degree (MD, JD, etc.)", "PhD"), "Yes", "No")) %>% 
  group_by(if_white) %>% 
  summarise(master_below = sum(higher_edu == "No"), master_or_above=sum(higher_edu == "Yes") )

ask_a_manager_2021_race

<<<<<<< HEAD ======= >>>>>>> XuejingHuang-master

if_white	master_below	master_or_above
Non-white	2256	1411
White	11623	8639
Non-white	2347	1513
White	11976	9063

prop.test(x = c(1513, 9063), n = c(1513+2347, 9063+11976), alternative = "two.sided")

## 
##  2-sample test for equality of proportions with continuity correction
## 
## data:  c out of c1513 out of 1513 + 23479063 out of 9063 + 11976
## X-squared = 20, df = 1, p-value = 8e-06
## alternative hypothesis: two.sided
## 95 percent confidence interval:
##  -0.0557 -0.0219
## sample estimates:
## prop 1 prop 2 
##  0.392  0.431

<<<<<<< HEAD

From the result of the proportion test, we cannot see a clear difference between the proportion of education level in white and non-white group, as the p-value is higher than 5%.

However, the proportion of acquiring higher education degree (Master’s-or-above) is higher in the white group vs. other races

Changed Industry? vs salary (Raghav)

ask_a_manager_2021_changed <- ask_a_manager_2021  %>%
mutate(if_changed= if_else(overall_years_of_professional_experience == years_of_experience_in_field, "No", "Yes"))

ask_a_manager_2021_changed %>% 
ggplot(aes(x=fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), y=annual_salary, color=if_changed))+
  # scatter plot
  geom_point(alpha = 0.5)+
  # linear smooth line
  geom_smooth(method = "lm")+
  # legend settings
  theme(legend.position = "bottom",
        legend.title = element_blank())+
  labs(title="Whether changing job affects your salary",
       x="Total Years Spent Working",
       y="Salary")+
  theme_bw()+
  NULL

ask_a_manager_2021_changed %>% 
  group_by(overall_years_of_professional_experience, if_changed) %>% 
  summarise (median_salary_over_time = median (salary),
             mean_salary_over_time = mean(salary)) %>% 
  ggplot () +
  geom_col(aes (fct_relevel(overall_years_of_professional_experience,levels=c("1 year or less","2 - 4 years","5-7 years","8 - 10 years","11 - 20 years","21 - 30 years","31 - 40 years","41 years or more")), median_salary_over_time, fill=if_changed), position = position_dodge(width = 0.9), alpha = 0.7)+
  geom_point(aes(x = overall_years_of_professional_experience, y=mean_salary_over_time, color = if_changed))+
  theme_bw()+
  #formatC(x, format = "d")+
  labs(  title =  "Median Salary over time" , y="Salary" ,x = "Prof") +
  NULL

ggsave("switch_industry.png",width = 12,height = 8,limitsize = F)

=======

Observations:

• From the result of the proportion test, we cannot see a clear difference between the proportion of education level in white and non-white group, as the p-value is higher than 5%.

• However, the proportion of acquiring higher education degree (Master’s-or-above) is higher in the white group vs. other races

Changed Industry vs salary

>>>>>>> XuejingHuang-master

t.test(salary~if_changed, data = ask_a_manager_2021_changed)

## 
##  Welch Two Sample t-test
## 
## data:  salary by if_changed
<<<<<<< HEAD
## t = 19, df = 23826, p-value <2e-16
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
##  7949 9827
## sample estimates:
##  mean in group No mean in group Yes 
##             85884             76996

======= ## t = 15, df = 24238, p-value <2e-16 ## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0 ## 95 percent confidence interval: ## 11569 15074 ## sample estimates: ## mean in group No mean in group Yes ## 95369 82047 >>>>>>> XuejingHuang-master

prop.test(~if_changed, data = ask_a_manager_2021_changed, success = "Yes")

## 
##  1-sample proportions test with continuity correction
## 
## data:  ask_a_manager_2021_changed$if_changed  [with success = Yes]
<<<<<<< HEAD
## X-squared = 137, df = 1, p-value <2e-16
## alternative hypothesis: true p is not equal to 0.5
## 95 percent confidence interval:
##  0.456 0.469
## sample estimates:
##     p 
## 0.462

======= ## X-squared = 198, df = 1, p-value <2e-16 ## alternative hypothesis: true p is not equal to 0.5 ## 95 percent confidence interval: ## 0.449 0.462 ## sample estimates: ## p ## 0.455 >>>>>>> XuejingHuang-master

ask_a_manager_age %>% 
  filter(age_group %in% c("18-24", "25-34" , "35-44" , "45-54" , "55-64"  )) %>% 
  group_by(age_group, gender) %>% 
  summarise (median_salary_by_age = median (salary),
             mean_salary_by_age = mean(salary)) %>% 
  ggplot () +
  geom_col(aes (age_group, median_salary_by_age, fill = gender), position = position_dodge(width = 0.9), alpha = 0.7)+
  geom_point(aes(x = age_group, y=mean_salary_by_age, color = gender))+
  theme_bw()+
  #formatC(x, format = "d")+
  labs(  title =  "Median Salary by Age Group" , y="Salary" ,x = "Age Group") +
  NULL

<<<<<<< HEAD

Regression Analysis (Yuan)

# load population data of US
population_url <- "https://www.ers.usda.gov/webdocs/DataFiles/48747/PopulationEstimates.csv?v=2232"
population <- vroom(population_url) %>% 
  janitor::clean_names() %>% 
  
  # select the latest data, namely 2019
  select(fips = fip_stxt, state= area_name, pop_estimate_2019) %>% 
  group_by(state) %>% 
  summarise(pop_estimate_2019_m = sum(pop_estimate_2019)/1000000)

reg_data <- ask_a_manager_2021 %>% 
  filter(!is.na(state)&industry %in% industry_list$industry[1:15]) %>%
  group_by(state) %>%
  filter(n()>10) %>% 
  ungroup() %>% 
  filter(country =="US"&currency=="USD") %>% 
  mutate(job = tolower(job_title),
         research=grepl("research", job),
         manager=grepl("manager", job),
         engineer=grepl("engineer", job),
         analyst=grepl("analyst", job),
         data_related = grepl("data", job),
         scientist = grepl("scientist", job),
         database = grepl("database", job),
         librarian = grepl("librarian", job),
         add_context_job = !is.na(additional_context_on_job_title),
         add_context_inc = !is.na(additional_context_on_income),
         race = case_when(race=="White"~"White",
                           race=="Asian or Asian American"~"Asian",
                           race=="Black or African American"~"Black",
                           T~"Others"),
         ) %>% 
  left_join(population,by = c("state")) %>% 
  select(c("age_group","gender","education","field_exp","pro_exp","race",
           "salary","research","manager","engineer","industry","analyst",
           "data_related","scientist","database","librarian","add_context_job",
           "add_context_inc","pop_estimate_2019_m"))
set.seed(2021)
data_split <- initial_split(reg_data, prop = .7)
data_train <- training(data_split)
data_test  <- testing(data_split)

m1 <- lm(salary ~ age_group, data=data_train)
m2 <- lm(salary ~ pro_exp, data=data_train)
m3 <- lm(salary ~ field_exp, data=data_train)

huxreg(m1, m2, m3,
       statistics = c('#observations' = 'nobs', 
                      'R squared' = 'r.squared', 
                      'Adj. R Squared' = 'adj.r.squared', 
                      'Residual SE' = 'sigma',
                      "AIC" = "AIC"), 
       bold_signif = 0.05, 
       stars = NULL
) %>% 
  set_caption('Comparison of models (age or experience?)')

Comparison of models (age or experience?)

	(1)	(2)	(3)
(Intercept)	62543.271	69163.194	64629.469
	(1830.256)	(2757.586)	(1593.274)
age_group25-34	19403.664
	(1903.262)
age_group35-44	29330.903
	(1921.417)
age_group45-54	30040.880
	(2092.886)
age_group55-64	24734.760
	(2580.640)
age_group65 or over	33200.979
	(5904.978)
pro_exp2 - 4 years		1748.729
		(2966.607)
pro_exp5-7 years		8977.174
		(2882.552)
pro_exp8 - 10 years		16197.606
		(2869.375)
pro_exp11 - 20 years		22579.448
		(2818.453)
pro_exp21 - 30 years		26381.364
		(2920.467)
pro_exp31 - 40 years		23567.026
		(3372.684)
pro_exp41 years or more		27874.097
		(6010.020)
field_exp2 - 4 years			7854.610
			(1755.638)
field_exp5-7 years			17648.936
			(1738.610)
field_exp8 - 10 years			26244.079
			(1780.904)
field_exp11 - 20 years			32552.800
			(1736.730)
field_exp21 - 30 years			39541.567
			(2069.468)
field_exp31 - 40 years			36020.849
			(3298.741)
field_exp41 years or more			37574.119
			(8921.330)
#observations	11366	11366	11366
R squared	0.032	0.044	0.085
Adj. R Squared	0.031	0.044	0.085
Residual SE	37240.202	36996.902	36192.227
AIC	271520.886	271373.884	270874.011

# add more variables to explore and improve accuracy
m4 <- lm(salary ~ field_exp+gender, data=data_train)
m5 <- lm(salary ~ field_exp+gender+gender*field_exp, data=data_train)
m6 <- lm(salary ~ field_exp+gender+education, data=data_train)
m7 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race, data=data_train)
m8 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m, data=data_train)
m9 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian, data=data_train)
m10 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian+add_context_job+add_context_inc, data=data_train)
m11 <- lm(salary ~ field_exp+gender+gender*field_exp+education+race+pop_estimate_2019_m++research+manager+engineer+industry+data_related+scientist+database+librarian+add_context_job+add_context_inc+data_related*scientist, data=data_train)

huxreg(m4, m5, m6, m7, m8, m9,m10,m11,
       statistics = c('#observations' = 'nobs', 
                      'R squared' = 'r.squared', 
                      'Adj. R Squared' = 'adj.r.squared', 
                      'Residual SE' = 'sigma',
                      "AIC" = "AIC"), 
       bold_signif = 0.05
) %>% 
  set_caption('Comparison of models (adding variables)')

=======

Regression Analysis

colnames(ask_a_manager_2021)

reg_data <- ask_a_manager_2021 %>% 
  mutate(country = ifelse(country=="US","US","Non-US"))

m1 <- lm(converted_salary ~ education, data=reg_data)
m2 <- lm(converted_salary ~ education + age_group, data=reg_data)
m3 <- lm(converted_salary ~ education + gender, data=reg_data)
m4 <- lm(converted_salary ~ education + age_group + gender + pro_exp + field_exp, data=reg_data)
m5 <- lm(converted_salary ~ education + age_group + gender + field_exp, data=reg_data)

huxreg(m1, m2, m3, m4, m5,
       statistics = c('#observations' = 'nobs', 
                      'R squared' = 'r.squared', 
                      'Adj. R Squared' = 'adj.r.squared', 
                      'Residual SE' = 'sigma'), 
       bold_signif = 0.05, 
       stars = NULL
) %>% 
  set_caption('Comparison of models')

Comparison of models (adding variables)

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
(Intercept)	81776.234 ***	66767.288 ***	61868.327 ***	65401.135 ***	56250.261 ***	45055.674 ***	45729.919 ***	46004.497 ***
	(1738.606)	(4349.167)	(3107.656)	(5110.917)	(5079.613)	(4432.007)	(4430.512)	(4429.567)
field_exp2 - 4 years	7365.950 ***	14727.862 **	7468.093 ***	16171.417 ***	16416.344 ***	13408.778 ***	13273.262 ***	13042.230 ***
	(1718.596)	(4723.099)	(1679.468)	(4573.759)	(4519.084)	(3863.048)	(3859.322)	(3858.449)
field_exp5-7 years	17031.018 ***	29579.448 ***	17169.720 ***	30678.029 ***	30514.167 ***	25868.954 ***	25696.546 ***	25568.189 ***
	(1702.015)	(4670.309)	(1664.254)	(4521.359)	(4467.297)	(3819.817)	(3816.083)	(3814.758)
field_exp8 - 10 years	25508.925 ***	42869.715 ***	25092.637 ***	44690.433 ***	44550.091 ***	39911.766 ***	39832.038 ***	39761.758 ***
	(1743.499)	(4750.322)	(1706.089)	(4600.562)	(4545.550)	(3887.572)	(3883.716)	(3882.214)
field_exp11 - 20 years	31267.272 ***	50166.357 ***	31735.211 ***	53236.895 ***	53467.642 ***	48612.318 ***	48629.040 ***	48474.059 ***
	(1700.925)	(4612.021)	(1665.665)	(4468.249)	(4414.833)	(3777.293)	(3773.497)	(3772.291)
field_exp21 - 30 years	37108.294 ***	61997.956 ***	38462.779 ***	65992.422 ***	66619.310 ***	60928.085 ***	60794.601 ***	60636.541 ***
	(2028.575)	(5051.790)	(1984.661)	(4894.019)	(4835.636)	(4137.108)	(4132.989)	(4131.622)
field_exp31 - 40 years	34403.010 ***	54043.409 ***	36292.138 ***	57742.688 ***	58226.660 ***	48416.441 ***	48423.945 ***	48263.287 ***
	(3229.693)	(7532.978)	(3156.417)	(7297.092)	(7209.882)	(6190.530)	(6184.007)	(6181.720)
field_exp41 years or more	33149.581 ***	44622.712 **	35725.205 ***	50250.211 ***	47405.928 **	44952.740 ***	44480.551 ***	44360.666 ***
	(8734.644)	(15065.956)	(8530.996)	(14583.504)	(14410.108)	(12310.473)	(12297.729)	(12292.830)
genderWoman	-19661.624 ***	-2451.366	-20999.464 ***	-2873.503	-3218.214	4562.148	4365.833	4216.714
	(881.228)	(4657.198)	(864.683)	(4509.702)	(4455.817)	(3809.001)	(3805.147)	(3803.902)
field_exp2 - 4 years:genderWoman		-8184.435		-9083.216	-8611.437	-6486.088	-6271.595	-6035.878
		(5068.882)		(4907.729)	(4849.118)	(4144.534)	(4140.488)	(4139.482)
field_exp5-7 years:genderWoman		-14283.390 **		-14158.716 **	-13537.090 **	-8833.913 *	-8565.761 *	-8430.754 *
		(5013.483)		(4853.222)	(4795.326)	(4097.453)	(4093.564)	(4092.134)
field_exp8 - 10 years:genderWoman		-20027.841 ***		-21015.794 ***	-20033.340 ***	-14416.339 ***	-14202.318 ***	-14112.790 ***
		(5105.162)		(4942.695)	(4883.940)	(4174.024)	(4169.914)	(4168.328)
field_exp11 - 20 years:genderWoman		-22032.484 ***		-23158.969 ***	-22633.855 ***	-15468.431 ***	-15399.157 ***	-15228.537 ***
		(4960.781)		(4803.804)	(4746.459)	(4057.543)	(4053.373)	(4052.094)
field_exp21 - 30 years:genderWoman		-30413.847 ***		-31079.001 ***	-30930.745 ***	-23740.630 ***	-23457.579 ***	-23280.688 ***
		(5524.145)		(5347.123)	(5283.183)	(4516.219)	(4511.873)	(4510.397)
field_exp31 - 40 years:genderWoman		-23074.275 **		-22696.050 **	-22510.589 **	-9148.091	-9115.536	-8848.865
		(8341.930)		(8076.384)	(7979.804)	(6844.468)	(6837.204)	(6834.962)
field_exp41 years or more:genderWoman		-11745.816		-13193.711	-11747.941	-4331.990	-4543.419	-4469.599
		(18526.974)		(17937.090)	(17722.788)	(15141.976)	(15126.328)	(15120.249)
educationSome college			4988.324	5044.075	4636.588	2908.226	2838.183	2817.131
			(2960.283)	(2927.504)	(2892.597)	(2472.678)	(2470.093)	(2469.106)
educationCollege degree			18988.963 ***	18564.373 ***	18103.897 ***	16120.258 ***	16007.010 ***	16004.845 ***
			(2707.906)	(2679.020)	(2647.125)	(2271.729)	(2269.421)	(2268.506)
educationMaster's degree			22035.333 ***	22003.130 ***	21773.117 ***	26169.277 ***	26051.736 ***	26020.462 ***
			(2727.491)	(2698.067)	(2665.835)	(2297.681)	(2295.354)	(2294.450)
educationProfessional degree (MD, JD, etc.)			44380.824 ***	44081.844 ***	43833.823 ***	50220.492 ***	50135.846 ***	50043.463 ***
			(3062.756)	(3029.627)	(2993.431)	(2679.716)	(2677.263)	(2676.341)
educationPhD			34253.662 ***	34347.772 ***	33870.649 ***	42625.750 ***	42423.359 ***	42460.846 ***
			(3038.775)	(3005.376)	(2969.572)	(2596.833)	(2594.869)	(2593.850)
raceBlack				-18402.690 ***	-15938.390 ***	-11313.766 ***	-11407.047 ***	-11518.091 ***
				(2545.570)	(2519.478)	(2155.689)	(2153.584)	(2152.998)
raceOthers				-17412.220 ***	-15957.584 ***	-10927.498 ***	-10967.692 ***	-11045.574 ***
				(1872.903)	(1852.565)	(1585.183)	(1583.814)	(1583.364)
raceWhite				-21718.588 ***	-18375.693 ***	-12618.968 ***	-12572.226 ***	-12689.214 ***
				(1558.149)	(1552.551)	(1329.726)	(1328.641)	(1328.614)
pop_estimate_2019_m					475.073 ***	386.143 ***	384.118 ***	382.987 ***
					(28.532)	(24.495)	(24.474)	(24.467)
researchTRUE						-883.355	-798.122	-289.189
						(1755.383)	(1753.641)	(1760.213)
managerTRUE						8051.040 ***	7987.988 ***	7987.374 ***
						(724.308)	(723.900)	(723.609)
engineerTRUE						22370.267 ***	22149.896 ***	22181.989 ***
						(1113.630)	(1113.308)	(1112.905)
industryBusiness or Consulting						13611.797 ***	13783.351 ***	13869.657 ***
						(1733.742)	(1732.446)	(1731.960)
industryComputing or Tech						20565.078 ***	20346.082 ***	20297.335 ***
						(1210.152)	(1210.497)	(1210.106)
industryEducation (Higher Education)						-25047.782 ***	-24855.369 ***	-24839.356 ***
						(1309.853)	(1309.556)	(1309.038)
industryEducation (Primary/Secondary)						-28805.465 ***	-29088.985 ***	-29049.552 ***
						(1676.035)	(1676.591)	(1675.961)
industryEngineering or Manufacturing						-3364.428 *	-3227.936 *	-3138.458 *
						(1470.645)	(1469.526)	(1469.203)
industryGovernment and Public Administration						-7400.718 ***	-7188.259 ***	-7040.606 ***
						(1387.959)	(1387.215)	(1387.431)
industryHealth care						-6140.234 ***	-6024.081 ***	-5887.018 ***
						(1347.309)	(1346.136)	(1346.283)
industryInsurance						-2263.661	-2285.620	-2297.536
						(1991.211)	(1989.459)	(1988.661)
industryLaw						-6175.522 ***	-6140.885 ***	-6108.539 ***
						(1790.010)	(1788.228)	(1787.536)
industryMarketing, Advertising & PR						-143.477	-288.065	-353.300
						(1586.731)	(1585.343)	(1584.837)
industryMedia & Digital						-4530.043 **	-4428.712 *	-4432.195 *
						(1744.371)	(1743.891)	(1743.189)
industryNonprofits						-16533.495 ***	-16367.934 ***	-16350.639 ***
						(1247.272)	(1246.407)	(1245.916)
industryRecruitment or HR						-3839.452	-4038.510 *	-4082.243 *
						(2058.276)	(2056.456)	(2055.673)
industryRetail						-21262.973 ***	-21306.608 ***	-21291.698 ***
						(2109.154)	(2107.490)	(2106.646)
data_relatedTRUE						7837.394 ***	7487.863 ***	5046.085 *
						(1921.203)	(1920.457)	(2067.321)
scientistTRUE						14722.590 ***	14689.589 ***	8820.476 **
						(2639.320)	(2636.522)	(3216.586)
databaseTRUE						-19977.998 **	-19842.340 **	-17405.812 **
						(6354.838)	(6348.374)	(6391.829)
librarianTRUE						-9214.392 ***	-9369.906 ***	-9397.497 ***
						(2193.651)	(2192.143)	(2191.277)
add_context_jobTRUE							-2807.597 ***	-2793.119 ***
							(627.314)	(627.077)
add_context_incTRUE							2827.822 **	2838.123 **
							(921.745)	(921.379)
data_relatedTRUE:scientistTRUE								17434.528 **
								(5478.061)
#observations	11366	11366	11366	11366	11366	11366	11366	11366
R squared	0.124	0.129	0.165	0.185	0.204	0.421	0.422	0.423
Adj. R Squared	0.123	0.128	0.164	0.183	0.203	0.419	0.420	0.421
Residual SE	35425.743	35332.798	34591.428	34191.042	33782.140	28843.094	28812.405	28800.792
AIC	270388.418	270335.691	269851.645	269596.977	269324.476	265752.352	265730.145	265721.976
*** p < 0.001; ** p < 0.01; * p < 0.05.

>>>>>>> XuejingHuang-master

Comparison of models

	(1)	(2)	(3)	(4)	(5)
(Intercept)	73753.850	62856.643	92165.971	70905.720	65882.024
	(3031.177)	(28236.014)	(3065.946)	(27781.746)	(27527.453)
educationSome college	1146.369	178.194	4490.817	4192.416	4395.104
	(3456.755)	(3426.821)	(3410.103)	(3333.414)	(3333.739)
educationCollege degree	10936.411	14478.221	16607.004	20368.221	20329.478
	(3096.939)	(3076.147)	(3060.401)	(3001.029)	(2997.480)
educationMaster's degree	16560.585	17400.650	23572.779	24328.428	24326.172
	(3128.923)	(3106.455)	(3095.672)	(3036.162)	(3030.978)
educationProfessional degree (MD, JD, etc.)	60645.762	60842.097	68008.053	70035.175	69643.884
	(3625.164)	(3597.836)	(3584.357)	(3518.621)	(3508.744)
educationPhD	30306.472	29312.578	36070.156	39374.432	38793.181
	(3597.624)	(3568.901)	(3553.194)	(3498.655)	(3478.807)
age_group18-24		-17669.678		-9879.743	-9267.739
		(28271.961)		(27567.347)	(27491.310)
age_group25-34		1111.921		-3876.079	-2008.487
		(28188.442)		(27463.620)	(27408.375)
age_group35-44		15983.686		-5471.914	-1500.703
		(28187.758)		(27457.952)	(27410.485)
age_group45-54		20370.007		-11070.008	-7739.091
		(28206.456)		(27461.839)	(27431.085)
age_group55-64		25471.862		-8051.878	-6100.110
		(28269.960)		(27527.157)	(27518.041)
age_group65 or over		17678.366		-14583.331	-14958.135
		(29120.832)		(28447.729)	(28432.793)
genderWoman			-30004.937	-26329.654	-26229.759
			(1117.505)	(1099.665)	(1099.280)
pro_exp2 - 4 years				-7805.873
				(4204.339)
pro_exp5-7 years				-5300.576
				(4351.911)
pro_exp8 - 10 years				-1392.495
				(4392.389)
pro_exp11 - 20 years				337.324
				(4472.274)
pro_exp21 - 30 years				-1275.784
				(4821.102)
pro_exp31 - 40 years				-2767.468
				(5781.403)
pro_exp41 years or more				-12687.960
				(9105.571)
field_exp2 - 4 years				8411.615	6500.929
				(2505.791)	(2225.984)
field_exp5-7 years				17208.320	16989.085
				(2596.075)	(2275.486)
field_exp8 - 10 years				25127.188	26530.695
				(2694.501)	(2345.796)
field_exp11 - 20 years				36182.740	37489.263
				(2725.230)	(2381.517)
field_exp21 - 30 years				57087.061	57240.743
				(3327.637)	(2920.774)
field_exp31 - 40 years				51244.416	50426.815
				(5212.230)	(4654.659)
field_exp41 years or more				49007.992	42568.136
				(12596.801)	(11634.336)
#observations	24899	24899	24899	24899	24899
R squared	0.028	0.047	0.056	0.101	0.100
Adj. R Squared	0.028	0.047	0.056	0.100	0.099
Residual SE	69651.152	68979.521	68665.310	67032.046	67051.976

# calculate rmse on test set of 11 models
models <- list(m1,m2,m3,m4,m5,m6,m7,m8,m9,m10,m11)
rmses <- c()
i <- 0
for (model in models){
  i = i + 1
  print(paste("model",i))
  preds <- predict(model, data_test)
  rmses[i] <- RMSE(
    pred = preds,
    obs = data_test$salary
    )
  print(paste("RMSE on train data:",summary(model)$sigma))
  print(paste("RMSE on test data:", rmses[i]))
  
  SS.total      <- with(data_test,sum((salary-mean(salary))^2))
  #SS.residual   <- sum(residuals(model)^2)
  SS.regression <- sum((preds-mean(data_test$salary))^2)
  #SS.total - (SS.regression+SS.residual)
  
  print(paste("R Squared on train data", summary(model)$r.squared))     
  print(paste("R Squared on test data", SS.regression/SS.total))  # fraction of variation explained by the model
  
}

## [1] "model 1"
## [1] "RMSE on train data: 37240.2022323879"
## [1] "RMSE on test data: 36621.1660285581"
## [1] "R Squared on train data 0.031567631824252"
## [1] "R Squared on test data 0.0325708057791624"
## [1] "model 2"
## [1] "RMSE on train data: 36996.9022754174"
## [1] "RMSE on test data: 36370.057981252"
## [1] "R Squared on train data 0.0443486164938196"
## [1] "R Squared on test data 0.0453616685733399"
## [1] "model 3"
## [1] "RMSE on train data: 36192.2273555412"
## [1] "RMSE on test data: 35422.5643215129"
## [1] "R Squared on train data 0.0854669794715471"
## [1] "R Squared on test data 0.0871167812847264"
## [1] "model 4"
## [1] "RMSE on train data: 35425.7430731334"
## [1] "RMSE on test data: 34605.928699271"
## [1] "R Squared on train data 0.123870171233949"
## [1] "R Squared on test data 0.128557774630173"
## [1] "model 5"
## [1] "RMSE on train data: 35332.7982526982"
## [1] "RMSE on test data: 34546.9809164511"
## [1] "R Squared on train data 0.12899864129965"
## [1] "R Squared on test data 0.135434006206996"
## [1] "model 6"
## [1] "RMSE on train data: 34591.4284469105"
## [1] "RMSE on test data: 33760.8724115322"
## [1] "R Squared on train data 0.165019603869794"
## [1] "R Squared on test data 0.172745944188134"
## [1] "model 7"
## [1] "RMSE on train data: 34191.0422206893"
## [1] "RMSE on test data: 33460.4589680199"
## [1] "R Squared on train data 0.184955679984239"
## [1] "R Squared on test data 0.195085008342806"
## [1] "model 8"
## [1] "RMSE on train data: 33782.1401858356"
## [1] "RMSE on test data: 33043.2494947125"
## [1] "R Squared on train data 0.204404031635445"
## [1] "R Squared on test data 0.219644384830675"
## [1] "model 9"
## [1] "RMSE on train data: 28843.0937046904"
## [1] "RMSE on test data: 28455.5022244996"
## [1] "R Squared on train data 0.421108713870185"
## [1] "R Squared on test data 0.440785215341321"
## [1] "model 10"
## [1] "RMSE on train data: 28812.4050521705"
## [1] "RMSE on test data: 28412.091845217"
## [1] "R Squared on train data 0.422441983244408"
## [1] "R Squared on test data 0.441748058491165"
## [1] "model 11"
## [1] "RMSE on train data: 28800.7921559706"
## [1] "RMSE on test data: 28397.9853883121"
## [1] "R Squared on train data 0.422958450110671"
## [1] "R Squared on test data 0.442305304536917"

# for model 10, comparing to 28657.84 RMSE in training data. 
# the RMSE 29507 is not very large indicating there is not very heavy over fitting problem